Causal Disturbance Analysis: A Novel Graph Centrality Based Method for Pathway Enrichment Analysis

Pathway enrichment analysis models (PEM) are the premier methods for interpreting gene expression profiles 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 interactions for 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 gene interactions to quantify the topological importance of genes' expression profiles in pathways 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 genes that are more likely to disturb a pathways functionality. CADIA infers an enrichment score for a pathway by deriving statistical evidence from Source/Sink centrality of the differentially expressed 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 gene-level changes that and provides an informative framework for interpreting high-throughput data.