{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/model-based-clustering-of-multi-tissue-gene","title":"Model-based clustering of multi-tissue gene expression data","arxiv_id":"1804.06911","date":"2018-04-18","proceeding":null,"authors":[],"abstract":"Recently, it has become feasible to generate large-scale, multi-tissue gene\nexpression data, where expression profiles are obtained from multiple tissues\nor organs sampled from dozens to hundreds of individuals. When traditional\nclustering methods are applied to this type of data, important information is\nlost, because they either require all tissues to be analyzed independently,\nignoring dependencies and similarities between tissues, or to merge tissues in\na single, monolithic dataset, ignoring individual characteristics of tissues.\nWe developed a Bayesian model-based multi-tissue clustering algorithm, revamp,\nwhich can incorporate prior information on physiological tissue similarity, and\nwhich results in a set of clusters, each consisting of a core set of genes\nconserved across tissues as well as differential sets of genes specific to one\nor more subsets of tissues. Using data from seven vascular and metabolic\ntissues from over 100 individuals in the STockholm Atherosclerosis Gene\nExpression (STAGE) study, we demonstrate that multi-tissue clusters inferred by\nrevamp are more enriched for tissue-dependent protein-protein interactions\ncompared to alternative approaches. We further demonstrate that revamp results\nin easily interpretable multi-tissue gene expression associations to key\ncoronary artery disease processes and clinical phenotypes in the STAGE\nindividuals. Revamp is implemented in the Lemon-Tree software, available at\nhttps://github.com/eb00/lemon-tree","url_abs":"http://arxiv.org/abs/1804.06911v1","url_pdf":"http://arxiv.org/pdf/1804.06911v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"model-based-clustering-of-multi-tissue-gene","repo_url":"https://github.com/eb00/lemon-tree","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}