Predicting mechanism of action of cellular perturbations with pathway activity signatures

Abstract

Motivation: Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem.

Results: We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail.

Availability and implementation: Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs.

Supplementary information: Supplementary data are available at Bioinformatics online.