Network constrained clustering for gene microarray data

Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways. Based on the construction of co-expression networks that consists of both significantly linear and nonlinear gene associations together with controlled biological and statistical significance, we can make accurate discovery of many transitively coexpressed genes and similarly coexpressed genes. Our approach tends to group functionally related genes into a tight cluster. We illustrate our approach and compare it to the traditional clustering approaches on a retinal gene expression dataset. The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www-personal.umich.edu//sup /spl sim//zhud/gene nt.htm/.