A Method to Identify Significant Clusters in Gene Expression Data

Katherine S. Pollard, Division of Biostatistics, School of Public Health, University of California, Berkeley
Mark Johannes van der Laan, Division of Biostatistics, School of Public Health, University of California, Berkeley

Abstract

Clustering algorithms have been widely applied to gene expression data. For both hierarchical and partitioning clustering algorithms, selecting the number of significant clusters is an important problem and many methods have been proposed. Existing methods for selecting the number of clusters tend to find only the global patterns in the data (e.g.: the over and under expressed genes). We have noted the need for a better method in the gene expression context, where small, biologically meaningful clusters can be difficult to identify. In this paper, we define a new criteria, Mean Split Silhouette (MSS), which is a measure of cluster heterogeneity. We propose to choose the number of clusters as the minimizer of MSS. In this way, the number of significant clusters is defined as that which produces the most homogeneous clusters. The power of this method compared to existing methods is demonstrated on simulated microarray data. The minimum MSS method is an example of a general approach that can be applied to any clustering routine with any global criteria.