Dynamic clustering of gene expression.

ISRN bioinformatics Pub Date : 2012-10-16 eCollection Date: 2012-01-01 DOI:10.5402/2012/537217
Lingling An, R W Doerge
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Abstract

It is well accepted that genes are simultaneously involved in multiple biological processes and that genes are coordinated over the duration of such events. Unfortunately, clustering methodologies that group genes for the purpose of novel gene discovery fail to acknowledge the dynamic nature of biological processes and provide static clusters, even when the expression of genes is assessed across time or developmental stages. By taking advantage of techniques and theories from time frequency analysis, periodic gene expression profiles are dynamically clustered based on the assumption that different spectral frequencies characterize different biological processes. A two-step cluster validation approach is proposed to statistically estimate both the optimal number of clusters and to distinguish significant clusters from noise. The resulting clusters reveal coordinated coexpressed genes. This novel dynamic clustering approach has broad applicability to a vast range of sequential data scenarios where the order of the series is of interest.

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基因表达的动态聚类
众所周知,基因同时参与多个生物过程,而基因在这些过程中是相互协调的。遗憾的是,以发现新基因为目的对基因进行分组的聚类方法未能认识到生物过程的动态性质,即使对不同时间或发育阶段的基因表达进行评估,也只能提供静态的聚类。利用时间频率分析的技术和理论,基于不同频谱频率代表不同生物过程的假设,对周期性基因表达谱进行动态聚类。我们提出了一种两步聚类验证方法,以统计估计最佳聚类数量,并将重要聚类与噪声区分开来。由此产生的聚类揭示了协调共表达的基因。这种新颖的动态聚类方法可广泛应用于对序列顺序感兴趣的各种序列数据场景。
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