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Bayesian generalized biclustering analysis via adaptive structured shrinkage.

Authors
  • Li, Ziyi1
  • Chang, Changgee2
  • Kundu, Suprateek1
  • Long, Qi2
  • 1 Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Road, NE, Atlanta, GA, USA.
  • 2 Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, USA.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Jul 01, 2020
Volume
21
Issue
3
Pages
610–624
Identifiers
DOI: 10.1093/biostatistics/kxy081
PMID: 30596887
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Biclustering techniques can identify local patterns of a data matrix by clustering feature space and sample space at the same time. Various biclustering methods have been proposed and successfully applied to analysis of gene expression data. While existing biclustering methods have many desirable features, most of them are developed for continuous data and few of them can efficiently handle -omics data of various types, for example, binomial data as in single nucleotide polymorphism data or negative binomial data as in RNA-seq data. In addition, none of existing methods can utilize biological information such as those from functional genomics or proteomics. Recent work has shown that incorporating biological information can improve variable selection and prediction performance in analyses such as linear regression and multivariate analysis. In this article, we propose a novel Bayesian biclustering method that can handle multiple data types including Gaussian, Binomial, and Negative Binomial. In addition, our method uses a Bayesian adaptive structured shrinkage prior that enables feature selection guided by existing biological information. Our simulation studies and application to multi-omics datasets demonstrate robust and superior performance of the proposed method, compared to other existing biclustering methods. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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