Model-based clustering of multi-tissue gene expression data.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 03 2020
01 03 2020
Historique:
received:
17
11
2018
revised:
05
09
2019
accepted:
31
10
2019
pubmed:
7
11
2019
medline:
17
9
2020
entrez:
6
11
2019
Statut:
ppublish
Résumé
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. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 31688915
pii: 5613175
doi: 10.1093/bioinformatics/btz805
pmc: PMC7162352
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1807-1813Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/P013732/1
Pays : United Kingdom
Organisme : NHLBI NIH HHS
ID : R01 HL125863
Pays : United States
Organisme : Cancer Research UK
ID : C18281/A19169
Pays : United Kingdom
Informations de copyright
© The Author(s) 2019. Published by Oxford University Press.
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