A New Clustering Method and Its Application to Proteomic Profiling for Colon Cancer

Y. Ou, L. Guo, and C.-Q. Zhang (USA)

Keywords

Biological Data Mining, unsupervised hierarchical clustering, overlapping clusters, Microarray Data Analysis, NCI60, chemosensitivity determinants.

Abstract

In this paper, we introduce a new clustering method: quasi-clique merger, and its associated data pretreatment programs. This program constructs non binary hierarchical trees with much smaller number of clusters in the outputs. And overlapping clusters are also allowed in the outputs. We applied this new method to cluster 60 human cancer cell lines (the NCI-60) using the previously identified proteomic determinants for chemosensitivity of 5-Fluorouracil (5-FU). All colon cancer cell lines were aggregated into a single cluster, indicating that the eight proteomic markers are potential diagnostic markers of colon cancer. The results based on the new clustering method have surpassed those based on previous methods on the same datasets.

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