A pathway impact analysis approach to identify context‐specific signaling networks consistent with differential gene expression data
Rajanikanth Vadigepalli · The FASEB Journal · 2011
The objective of the present study is to integrate the information on signaling network structures with differential gene expression data sets to predict functional changes in the cellular pathways that are operational under those conditions. We developed a novel approach that starts with a user‐specified pathway structure, generates thousands of alternative pathway structures and evaluates each network for consistency with the gene expression data based on the statistical significance of a perturbation impact factor metric. Our approach removes the interactions in the initial network that are not consistent with the gene expression changes, and yields context‐specific signaling pathways. We benchmarked our approach using biologically motivated synthetic networks as well as a Gq‐protein coupled receptor pathway. Our results indicate that (1) networks with high consistency are likely to arise by differential expression of activators and inhibitors in opposing directions, and (2) nodes with upstream activation and inhibitory interactions significantly affect the network consistency. Research Support: NIH ‐ R01 GM083108 and R33 HL088283.