A Heuristic Approach to Scoring Gene Clustering Algorithms

In the past decades, many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. Given the same data set, different clustering algorithms can potentially generate very different clusters. A biologist with a gene expression data set is faced with the problem of choosing an appropriate clustering algorithm for his or her data set. In this paper, we present a new tool that allows the similarity analysis of clusters generated by different algorithms. This tool may: (1) improve the quality of the data analysis results, (2) support the prediction of the number of relevant clusters in the Microarray datasets, and (3) provide cross-reference between different algorithms. The software tool can also be used to analyze cluster similarities from other biomedical data. We demonstrate the use of this tool with gene expression data of Leukaemia and Sporulation.

• Type: Conference
• Year: 2006
• Where: BIOCOMP
• Authors: Longde Yin, Chun-Hsi Huang
• Keywords: BIOCOMP 2006, BIOCOMP 2007, Clustering Algorithm, Data Set, Gene Expression Data