Gene coexpression network discovery with controlled statistical and biological significance

Many biological functions are executed as a module of coexpressed genes which can be conveniently viewed as a coexpression network. Genes are network vertices and significant pairwise coexpression are network edges. Traditional network discovery methods control either statistical significance or biological significance, but not both. We have designed and implemented a two-stage algorithm that controls both the statistical significance (false discovery rate, FDR) and the biological significance (minimum acceptable strength, MAS) of the discovered network. Based on the estimation of pairwise gene profile correlation, the algorithm provides an initial network discovery that controls only FDR, which is then followed by a second network discovery which controls both FDR and MAS. We illustrate the algorithm for discovery of coexpression networks for yeast galactose metabolism with controlled FDR and MAS.