On functional annotation with gene co-expression networks

Abstract

Gene co-expression networks have frequently been used for functional annotation. In these networks, an unknown gene is annotated with terms that have already been associated with genes whose expression profiles t end to correlate with the expression profile of the unknown gene. Despite the biological plausibility of this principle referred to as guilt-by-association, its applicability has not been thoroughly experimentally verified yet. In our paper, we formulate several statistical hypotheses concerning the principle and test them on a representative expression dataset. We demonstrate that gene annotation carried out with co-expression networks clearly outperforms random annotation and improves with increasing sample size and the knowledge of gene co-location. Eventually, we discuss the practical significance of this way of functional annotation.