Estimation of Identification Methods of Gene Clusters Using GO Term Annotations from a Hierarchical Cluster Tree

摘要

The hierarchical clustering algorithm has frequently been applied to grouping genes sharing a certain characteristic from a microarray data set. Identification of clusters from a hierarchical cluster tree is generally conducted by cutting the tree at a certain level. In this method, the most parent clusters are identified as mutually correlated gene groups and their child clusters are ignored. However the child clusters have a possibility to show more significant GO term annotation than their parent clusters. To overcome this problem, Toronen developed a novel algorithm based on the calculation of each GO annotation in all the clusters that satisfy a threshold of correlation coefficient. However comparison of the algorithm with the general method have not been done enough so far. Therefore we compared the general method with Toronen's proposed algorithm for identifying over-represented GO terms, and confirmed availability of the proposed algorithm.