基因表达数据的聚类分析研究进展  

State-of-the-art of Cluster Analysis of Gene Expression Data

作　　者：岳峰[1] 孙亮[2] 王宽全[1] 王永吉[2] 左旺孟[1] 

机构地区：[1]哈尔滨工业大学计算机学院生物信息技术研究中心,哈尔滨150001 [2]中国科学院软件研究所互联网软件技术实验室,北京100080

出　　处：《自动化学报》2008年第2期113-120,共8页Acta Automatica Sinica

基　　金：国家高技术研究发展计划(863计划)(2006AA012308);国家自然科学基金(60373053，60571025)资助~~

摘　　要：基因表达数据的爆炸性增长迫切需求自动、有效的数据分析工具.目前聚类分析己成为分析基因表达数据获取生物学信息的有力工具.为了更好地挖掘基因表达数据,近年来提出了许多改进的传统聚类算法和新聚类算法.本文首先简单介绍了基因表达数据的获取和表示,之后系统地介绍了近年来应用在基因表达数据分析中的聚类算法.根据聚类目标的不同将算法分为基于基因的聚类、基于样本的聚类和两路聚类,并对每类算法介绍了其生物学的含义及其难点,详细讨论了各种算法的基本原理及优缺点.最后总结了当前的基因表达数据的聚类分析方法,并对发展趋势作了进一步的展望.The flood of gene expression data provided by the DNA microarray technology has driven the development of automated analysis techniques and tools. Cluster analysis is an effective and practical method to mine the huge amount of gene expression data to gain important genetic and biological information. Many improved conventional clustering algorithms as well as new clustering algorithms have been proposed recently to process the gene expression data. This survey first introduces how to produce and represent the gene expression data, and then discusses the state-of-the-art cluster algorithms applied to gene expression data. According to the goals of clustering, clustering algorithms are divided into three categories： gene-based clustering, sample-based clustering, and biclustering. Basic biological principles and challenges for each category are presented. For each category, the basic principle is discussed in detail as well as its advantages and drawbacks. This paper concludes with a summarization in this field and a discussion of future trends.

关 键 词：DNA微阵列 基因表达数据 聚类分析 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]