A Bayesian semiparametric factor analysis model for subtype identification

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...

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Bibliographic Details
Published inStatistical applications in genetics and molecular biology Vol. 16; no. 2; pp. 145 - 158
Main Authors Sun, Jiehuan, Warren, Joshua L., Zhao, Hongyu
Format Journal Article
LanguageEnglish
Published Germany De Gruyter 25.04.2017
Walter de Gruyter GmbH
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ISSN2194-6302
1544-6115
1544-6115
DOI10.1515/sagmb-2016-0051

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Summary: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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ISSN:2194-6302
1544-6115
1544-6115
DOI:10.1515/sagmb-2016-0051