CONDITION-RELATED GENE FUNCTIONAL MODULE CLUSTER ANALYSIS
与实验条件相关的基因功能模块聚类分析方法

Given the phenomenon of gene functional modulization in a cell, the concepts of gene functional module and characteristic functional module were formally defined. In view of these two novel concepts, the condition-related gene functional module clustering algorithm was developed, which clusters genes into conditional-related gene functional modules based on both gene function knowledge and gene expression data. Increasing level of artificial noise was added into the original gene expression dataset and the stability of various functional modules was compared, and those functional modules which were most resistant to the data noise were extracted as the characteristic functional modules. It was demonstrated with the adding-noise experiment that the condition-related gene functional module clustering algorithm was superior to both hierarchical clustering and fuzzy C-means clustering with respect to their stability against data noise that was commonly found in microarray technology. Eight characteristic functional modules were extracted when the algorithm was applied to NCI60 gene expression data.