FOCUS Clustering of protein expression data: a benchmark of statistical and neural approaches

Abstract Clustering issues are fundamental to explor-atory analysis of bioinformatics data. This process may follow algorithms that are reproducible but make assump-tions about, for instance, the ability to estimate the global structure by successful local agglomeration or alterna-tively, they use pattern recognition methods that are sen-sitive to the initial conditions. This paper reviews two clustering methodologies and highlights the differences that result from the changes in data representation, applied to a protein expression data set for breast cancer (n = 1,076). The two clustering methodologies are a reproducible approach to model-free clustering and a probabilistic competitive neural network. The results from the two methods are compared with existing studies of the same data set, and the preferred clustering solutions are profiled for clinical interpretation