Title: Model-based clustering of multi-tissue gene expression data,
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
this https URL