Evaluation of clustering algorithms for gene expression data using gene ontology annotations  

作　　者：MA Ning ZHANG Zheng-guo 

机构地区：[1]Department of Biomedical Engineering,Institute of Basic Medical Sciences,Chinese Academy of Medical Sciences,School of Basic Medicine,Peking Union Medical College,Beijing 100005,China

出　　处：《Chinese Medical Journal》2012年第17期3048-3052,共5页中华医学杂志（英文版）

摘　　要：Background Clustering is a useful exploratory technique for interpreting gene expression data to reveal groups of genes sharing common functional attributes. Biologists frequently face the problem of choosing an appropriate algorithm. We aimed to provide a standalone, easily accessible and biologically oriented criterion for expression data clustering evaluation. Methods An external criterion utilizing annotation based similarities between genes is proposed in this work. Gene ontology information is employed as the annotation source. Comparisons among six widely used clustering algorithms over various types of gene expression data sets were carried out based on the criterion proposed. Results The rank of these algorithms given by the criterion coincides with our common knowledge. Single-linkage has significantly poorer performance, even worse than the random algorithm. Ward＇s method archives the best performance in most cases. Conclusions The criterion proposed has a strong ability to distinguish among different clustering algorithms with different distance measurements. It is also demonstrated that analyzing main contributors of the criterion may offer some guidelines in finding local compact clusters. As an addition, we suggest using Ward＇s algorithm for gene expression data analysis.Background Clustering is a useful exploratory technique for interpreting gene expression data to reveal groups of genes sharing common functional attributes. Biologists frequently face the problem of choosing an appropriate algorithm. We aimed to provide a standalone, easily accessible and biologically oriented criterion for expression data clustering evaluation. Methods An external criterion utilizing annotation based similarities between genes is proposed in this work. Gene ontology information is employed as the annotation source. Comparisons among six widely used clustering algorithms over various types of gene expression data sets were carried out based on the criterion proposed. Results The rank of these algorithms given by the criterion coincides with our common knowledge. Single-linkage has significantly poorer performance, even worse than the random algorithm. Ward＇s method archives the best performance in most cases. Conclusions The criterion proposed has a strong ability to distinguish among different clustering algorithms with different distance measurements. It is also demonstrated that analyzing main contributors of the criterion may offer some guidelines in finding local compact clusters. As an addition, we suggest using Ward＇s algorithm for gene expression data analysis.

关 键 词：MICROARRAY gene expression CLUSTERING gene ontology 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] Q-332[自动化与计算机技术—控制科学与工程]