基于贝叶斯模型的时间序列数据紧密聚类
Bayesian model-based tight clustering for time course data.
作者信息
Joo Yongsung, Casella G, Hobert J
机构信息
Department of Statistics, Dongguk University, Seoul 100-715, Korea,
出版信息
Comput Stat. 2010 Mar;25(1):17-38. doi: 10.1007/s00180-009-0159-7.
Abstract
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.
摘要
聚类分析已被广泛用于探索来自微阵列分析的数千个基因表达，并识别少量相似基因（对象）以进行进一步的详细生物学研究。然而，大多数聚类算法倾向于识别包含过多基因的松散聚类。在本文中，我们提出了一种用于时间序列基因表达数据的贝叶斯紧密聚类方法，该方法选择少量密切相关的基因，并仅用这些密切相关的基因构建紧密聚类。
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