Discovery of gene network variability across samples representing multiple classes - Ko, Younhee (2010)
Gene expression profiles from microarray experiments that include samples or biological replicates representing various classes, groups or states (e.g. treatments, developmental stages, health status) have been used to predict gene networks. To further mine the information from samples within and across classes, a framework that integrates Bayesian networks, mixture of gene co-expression models and clustering using on all the genes in the network is proposed. The approach was evaluated on two independent pathways using data from two microarray experiments. The postulated algorithm succeeded on reconstructing the topology of the gene pathways when benchmarked against empirical reports and randomized data sets. The majority or all the samples within a class shared the same co-expression model and were classified within the corresponding class. Our approach uncovered both, gene relationships and profiles that are unique to a particular class or shared across classes.