A novel clustering approach and prediction of optimal number of clusters: global optimum search with enhanced positioning

  • Authors:

  • Meng Piao Tan;James R. Broach;Christodoulos A. Floudas

  • Affiliations:

  • Department of Chemical Engineering, Princeton University, Princeton, USA 08544;Department of Molecular Biology, Princeton University, Princeton, USA 08544;Department of Chemical Engineering, Princeton University, Princeton, USA 08544

  • Venue:
  • Journal of Global Optimization
  • Year:
  • 2007

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Abstract

Cluster analysis of genome-wide expression data from DNA microarray hybridization studies is a useful tool for identifying biologically relevant gene groupings (DeRisi et al. 1997; Weiler et al. 1997). It is hence important to apply a rigorous yet intuitive clustering algorithm to uncover these genomic relationships. In this study, we describe a novel clustering algorithm framework based on a variant of the Generalized Benders Decomposition, denoted as the Global Optimum Search (Floudas et al. 1989; Floudas 1995), which includes a procedure to determine the optimal number of clusters to be used. The approach involves a pre-clustering of data points to define an initial number of clusters and the iterative solution of a Linear Programming problem (the primal problem) and a Mixed-Integer Linear Programming problem (the master problem), that are derived from a Mixed Integer Nonlinear Programming problem formulation. Badly placed data points are removed to form new clusters, thus ensuring tight groupings amongst the data points and incrementing the number of clusters until the optimum number is reached. We apply the proposed clustering algorithm to experimental DNA microarray data centered on the Ras signaling pathway in the yeast Saccharomyces cerevisiae and compare the results to that obtained with some commonly used clustering algorithms. Our algorithm compares favorably against these algorithms in the aspects of intra-cluster similarity and inter-cluster dissimilarity, often considered two key tenets of clustering. Furthermore, our algorithm can predict the optimal number of clusters, and the biological coherence of the predicted clusters is analyzed through gene ontology.