A spectral clustering method for microarray data

This paper considers a clustering method motivated by a multivariate analysis of variance model and computationally based on eigenanalysis (thus the term “spectral” in the title). Our focus is on large problems, and we present the method in the context of clustering genes using microarray expression data. We provide an efficient computational algorithm and discuss its properties and interpretation in statistical and geometric terms. Leukemia and Melanoma data sets are analyzed to demonstrate the use of the method, and simulations are carried out to compare our method with two other clustering algorithms. We extend the method to enable supervision by either gene or array characteristics.