Robust knowledge-guided biclustering for multi-omics data

dc.contributor.authorZhang, Qiyiwen
dc.contributor.authorChang, Changgee
dc.contributor.authorLong, Qi
dc.contributor.departmentBiostatistics and Health Data Science, School of Medicine
dc.date.accessioned2024-05-10T11:23:56Z
dc.date.available2024-05-10T11:23:56Z
dc.date.issued2023
dc.description.abstractBiclustering is a useful method for simultaneously grouping samples and features and has been applied across various biomedical data types. However, most existing biclustering methods lack the ability to integratively analyze multi-modal data such as multi-omics data such as genome, transcriptome and epigenome. Moreover, the potential of leveraging biological knowledge represented by graphs, which has been demonstrated to be beneficial in various statistical tasks such as variable selection and prediction, remains largely untapped in the context of biclustering. To address both, we propose a novel Bayesian biclustering method called Bayesian graph-guided biclustering (BGB). Specifically, we introduce a new hierarchical sparsity-inducing prior to effectively incorporate biological graph information and establish a unified framework to model multi-view data. We develop an efficient Markov chain Monte Carlo algorithm to conduct posterior sampling and inference. Extensive simulations and real data analysis show that BGB outperforms other popular biclustering methods. Notably, BGB is robust in terms of utilizing biological knowledge and has the capability to reveal biologically meaningful information from heterogeneous multi-modal data.
dc.eprint.versionFinal published version
dc.identifier.citationZhang Q, Chang C, Long Q. Robust knowledge-guided biclustering for multi-omics data. Brief Bioinform. 2023;25(1):bbad446. doi:10.1093/bib/bbad446
dc.identifier.urihttps://hdl.handle.net/1805/40635
dc.language.isoen_US
dc.publisherOxford University Press
dc.relation.isversionof10.1093/bib/bbad446
dc.relation.journalBriefings in Bioinformatics
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourcePMC
dc.subjectBiclustering
dc.subjectBayesian hierarchical model
dc.subjectMulti-view data
dc.titleRobust knowledge-guided biclustering for multi-omics data
dc.typeArticle
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