Learning Phenotype Specific Gene Network by Knowledge Driven Matrix Factorization

A popular method for reconstructing gene networks from micro-array data is Bayesian structure learning. However, most Bayesian structure learning algorithms suffer from three major shortcomings, i.e., the high computational cost, inefficiency in exploring qualitative knowledge, and inability of reconstructing phenotype specific gene network. We address these three short-comings by presenting a new framework, which first identifies the genes relevant to the given phenotype using a mixture regression model, and then reconstructs the network for the selected genes with a Knowledge driven Matrix Factorization (KMF) algorithm. We applied the proposed framework to gene expression and phenotypic data and identified highly enriched gene clusters with distinct cellular functions and processes together with the interactions between the clusters. Most of the interactions predicted by KMF were indeed biologically relevant. In summary, we have developed a framework that can correctly reconstruct the gene network that is specific to a given phenotype.