Clustering approaches for dealing with multiple DNA microarray datasets

This paper centres on clustering approaches that deal with multiple DNA microarray datasets. Four clustering algorithms for deriving a clustering solution from multiple gene expression matrices studying the same biological phenomenon are considered: two unsupervised cluster techniques based on information integration, a hybrid consensus clustering method combining Particle Swarm Optimization and k-means that can be referred to supervised clustering, and a supervised consensus clustering algorithm enhanced by Formal Concept Analysis (FCA), which initially produces a list of different clustering solutions, one per each experiment and then these solutions are transformed by portioning the cluster centres into a single overlapping partition, which is further analyzed by employing FCA. The four algorithms are evaluated on gene expression time series obtained from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe.