DynSig: Modelling Dynamic Signaling Alterations along Gene Pathways for Identifying Differential Pathways
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Machine Intelligence & Computational Biology Lab., Institute of Intelligent Machines, Chinese Academy of Science, P.O. Box 1130, Hefei 230031, China
School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230022, China
Authors to whom correspondence should be addressed.
Genes 2018, 9(7), 323; https://doi.org/10.3390/genes9070323
Received: 12 May 2018 / Revised: 25 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
(This article belongs to the Special Issue Selected Papers from the Bioinformatics and Intelligent Information Processing Conference (BIIP2018))
Although a number of methods have been proposed for identifying differentially expressed pathways (DEPs), few efforts consider the dynamic components of pathway networks, i.e., gene links. We here propose a signaling dynamics detection method for identification of DEPs, DynSig, which detects the molecular signaling changes in cancerous cells along pathway topology. Specifically, DynSig relies on gene links, instead of gene nodes, in pathways, and models the dynamic behavior of pathways based on Markov chain model (MCM). By incorporating the dynamics of molecular signaling, DynSig allows for an in-depth characterization of pathway activity. To identify DEPs, a novel statistic of activity alteration of pathways was formulated as an overall signaling perturbation score between sample classes. Experimental results on both simulation and real-world datasets demonstrate the effectiveness and efficiency of the proposed method in identifying differential pathways.