Incorporating Gene Significance in the Impact Analysis of Signaling Pathways

Abstract

Identification of the most impacted signaling pathways in a given condition is a crucial step in understanding the underlying biological mechanism. An impact analysis that is able to take in consideration the structure of a given signaling pathway was proposed to measure the impact on each pathway given a list of differentially expressed (DE) genes and their fold changes. Here, we investigated the utility of incorporating the individual gene significance in the impact analysis of signaling pathways. We propose two alternative models to incorporate the individual gene p-values and compare their performance over a pool of 24 datasets. In addition, the two new models offer the ability to work with the entire set of gene expression measurements, thus eliminating the need to select differentially expressed genes.