Bayesian model-based tight clustering for time course data

被引：2

作者：

Joo, Yongsung ^{[1
]}

Casella, George ^{[2
]}

Hobert, James ^{[2
]}

机构：

[1] Dongguk Univ, Dept Stat, Seoul 100715, South Korea

[2] Univ Florida, Dept Stat, Gainesville, FL 32611 USA

来源：

COMPUTATIONAL STATISTICS | 2010年 / 25卷 / 01期

关键词：

Bayesian cluster analysis; Tight clustering; Time course gene expression; Microarray; GENE-EXPRESSION DATA; MIXTURE MODEL; MICROARRAY; SELECTION;

D O I：

10.1007/s00180-009-0159-7

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Cluster analysis has been widely used to explore thousands of gene expressions from microarray analysis and identify a small number of similar genes (objects) for further detailed biological investigation. However, most clustering algorithms tend to identify loose clusters with too many genes. In this paper, we propose a Bayesian tight clustering method for time course gene expression data, which selects a small number of closely-related genes and constructs tight clusters only with these closely-related genes.

引用

页码：17 / 38

页数：22

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