Intelligent Kernel K-Means for Clustering Gene Expression

Abstract

Abstract Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using gene expression of human colorectal carcinoma had shown that the genes were grouped into three clusters. Global silhouette value and davies-bouldin index of the resulted clusters indicated that they are trustworthy and compact. To analyze the relationship between the clustered genes and phenotypes of clinical data, we performed correlation (CR) between each of three phenotypes (distant metastasis, cancer and normal tissues, and lymph node) with genes in each cluster of original dataset and permuted dataset. The result of the correlation had shown that Cluster 1 and Cluster 2 of original dataset had significantly higher CR than that of the permuted dataset. Among the three clusters, Cluster 3 contained smallest number of genes, but 16 out of 21 genes in that cluster were genes listed in Tumor Classifier List (TCL).