A Bayesian semiparametric factor analysis model for subtype identification.

IF 0.8 4区 数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Statistical Applications in Genetics and Molecular Biology Pub Date : 2017-04-25 DOI:10.1515/sagmb-2016-0051
Jiehuan Sun, Joshua L Warren, Hongyu Zhao
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Abstract

Disease subtype identification (clustering) is an important problem in biomedical research. Gene expression profiles are commonly utilized to infer disease subtypes, which often lead to biologically meaningful insights into disease. Despite many successes, existing clustering methods may not perform well when genes are highly correlated and many uninformative genes are included for clustering due to the high dimensionality. In this article, we introduce a novel subtype identification method in the Bayesian setting based on gene expression profiles. This method, called BCSub, adopts an innovative semiparametric Bayesian factor analysis model to reduce the dimension of the data to a few factor scores for clustering. Specifically, the factor scores are assumed to follow the Dirichlet process mixture model in order to induce clustering. Through extensive simulation studies, we show that BCSub has improved performance over commonly used clustering methods. When applied to two gene expression datasets, our model is able to identify subtypes that are clinically more relevant than those identified from the existing methods.

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一种用于亚型识别的贝叶斯半参数因子分析模型。
疾病亚型识别(聚类)是生物医学研究中的一个重要问题。基因表达谱通常用于推断疾病亚型,这通常会导致对疾病有生物学意义的见解。尽管已有的聚类方法取得了许多成功,但当基因高度相关且由于高维数而包含许多无信息的基因时,现有的聚类方法可能表现不佳。在本文中,我们介绍了一种新的基于基因表达谱的贝叶斯亚型鉴定方法。这种方法被称为BCSub,它采用一种创新的半参数贝叶斯因子分析模型,将数据的维数降至几个因子得分进行聚类。具体来说,为了诱导聚类,假设因子得分遵循Dirichlet过程混合模型。通过大量的仿真研究,我们表明BCSub比常用的聚类方法具有更高的性能。当应用于两个基因表达数据集时,我们的模型能够识别出比现有方法更具有临床相关性的亚型。
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来源期刊
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
自引率
11.10%
发文量
8
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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