IEEE/ACM Trans Comput Biol Bioinform 2019 Mar 25. Epub 2019 Mar 25.
Pathway enrichment analysis models (PEM) are the premier methods for interpreting genetic perturbations from high-throughput experiments. PEM often use a priori background knowledge to infer the underlying biological functions and mechanisms. A shortcoming of standard PEM is their disregarding of genetic interactions for mathematical simplicity, which potentially results in partial and inaccurate inference. In this study, we introduce a graph-based PEM, namely Causal Disturbance Analysis (CADIA), that leverages genetic interactions to quantify the topological importance of perturbations in pathway organizations. In particular, CADIA uses a novel graph centrality model, namely Source/Sink, to measure the topological importance. Source/Sink Centrality quantifies a gene's importance as a receiver and a sender of biological information, which allows for prioritizing the perturbations that are more likely to disturb a pathway's functionality. CADIA infers an enrichment score for a pathway by deriving statistical evidence from Source/Sink centrality of the perturbed genes and combines it with classical over-representation analysis. Through real-world experimental and synthetic data evaluations, we show that CADIA can uniquely infer critical pathway enrichments that are not observable through other PEM. Our results indicate that CADIA is sensitive towards topologically central perturbations and provides a robust framework for interpreting high-throughput data.