Robust knowledge-guided biclustering for multi-omics data

被引:1
|
作者
Zhang, Qiyiwen [1 ,3 ]
Chang, Changgee [2 ]
Long, Qi [1 ,3 ]
机构
[1] Univ Penn, Dept Biostat Epidemiol & Informat, Philadelphia, PA USA
[2] Indiana Univ Sch Med, Dept Biostat & Hlth Data Sci, Indianapolis, IN USA
[3] Univ Penn, Dept Biostat Epidemiol & Informat, Philadelphia, PA 19104 USA
基金
美国国家卫生研究院;
关键词
biclustering; bayesian hierarchical model; multi-view data; MCMC; ALGORITHMS; SELECTION; SPIKE;
D O I
10.1093/bib/bbad446
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Biclustering 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.
引用
收藏
页数:8
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