Hakamada Kazumi, Okamoto Masahiro, Hanai Taizo
Graduate School of Systems Life Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan.
Bioinformatics. 2006 Apr 1;22(7):843-8. doi: 10.1093/bioinformatics/btl016. Epub 2006 Jan 24.
Classifying genes into clusters depending on their expression profiles is one of the most important analysis techniques for microarray data. Because temporal gene expression profiles are indicative of the dynamic functional properties of genes, the application of clustering analysis to time-course data allows the more precise division of genes into functional classes. Conventional clustering methods treat the sampling data at each time point as data obtained under different experimental conditions without considering the continuity of time-course data between time periods t and t+1. Here, we propose a method designated mathematical model-based clustering (MMBC).
The proposed method, designated MMBC, was applied to artificial data and time-course data obtained using Saccharomyces cerevisiae. Our method is able to divide data into clusters more accurately and coherently than conventional clustering methods. Furthermore, MMBC is more tolerant to noise than conventional clustering methods.
Software is available upon request.
根据基因的表达谱将基因分类为簇是微阵列数据最重要的分析技术之一。由于基因的时间表达谱指示了基因的动态功能特性，将聚类分析应用于时间序列数据可以使基因更精确地划分为功能类别。传统的聚类方法将每个时间点的采样数据视为在不同实验条件下获得的数据，而不考虑时间段t和t + 1之间时间序列数据的连续性。在此，我们提出一种名为基于数学模型的聚类（MMBC）的方法。
所提出的名为MMBC的方法应用于人工数据和使用酿酒酵母获得的时间序列数据。我们的方法能够比传统聚类方法更准确、更连贯地将数据划分为簇。此外，MMBC比传统聚类方法对噪声更具耐受性。
可根据要求提供软件。
