University of Oxford, UK
Title: Evaluating modules in molecular networks in light of annotation bias
Abstract: Many complex systems can be represented as networks and the detection of novel functional modules in such networks has become an important step in many research areas. In this talk I will describe a method for evaluating potential modules that overcomes annotation biases that often occur in networks. I will demonstrate its utility in the area of biological networks. In biological networks, in the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as I will show, the uneven distribution of functional annotations means that such evaluation methods favour communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker’s ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities, and identifies functional modules that may correspond to more novel biology. If there is time at the end of the seminar I will also discuss a method for network comparison that we have recently developed that is aimed at identifying common organizational principles in networks. The methodology is simple, intuitive and is applicable in a wide variety of settings ranging from the functional classiﬁcation of proteins to tracking the evolution of the world trade network.