An optimal hierarchical clustering algorithm for gene expression data

被引：12
|
作者：
Seal, S [1 ]
Komarina, S [1 ]
Aluru, S [1 ]
机构：
[1] Iowa State Univ Sci & Technol, Dept Elect & Comp Engn, Ames, IA 50011 USA
来源：
INFORMATION PROCESSING LETTERS | 2005年 / 93卷 / 03期
关键词：
algorithms; computational geometry; gene expression; hierarchical clustering; microarrays;
D O I：
10.1016/j.ipl.2004.11.001
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Microarrays are used for measuring expression levels of thousands of genes simultaneously. Clustering algorithms are used on gene expression data to find co-regulated genes. An often used clustering strategy is the Pearson correlation coefficient based hierarchical clustering algorithm presented in [Proc. Nat. Acad. Sci. 95 (25) (1998) 14863-14868], which takes O(N-3) time. We note that this run time can be reduced to O(N-2) by applying known hierarchical clustering algorithms [Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms, 1998, pp. 619-628] to this problem. In this paper. we present an algorithm which runs in O(N log N) time using a geometrical reduction and show that it is optimal. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：143 / 147
页数：5
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