Abstract
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.
Highlights
With the rapid development of high-throughput technology, including microarrays and deep sequencing, tremendous amounts of various omics data have been generated and accumulated, which provides unprecedented opportunities for understanding molecular mechanisms of cells and disease etiology [1,2,3,4,5]
Current methods for identifying differentially expressed pathways (DEPs) can be categorized into three generations [9]: overrepresentation analysis, functional class scoring, and pathway topology
We propose to model gene links to capture biological signaling along them, and detect the dynamic alteration of pathway topology for identifying DEPs (DynSig)
Summary
With the rapid development of high-throughput technology, including microarrays and deep sequencing, tremendous amounts of various omics data have been generated and accumulated, which provides unprecedented opportunities for understanding molecular mechanisms of cells and disease etiology [1,2,3,4,5]. Identifying differentially expressed genes (DEGs) often suffers from being less statistically reproducible and less biologically interpretable, while identifying differentially expressed pathways (DEPs) provides more consistent and more reliable knowledge about cancer or other diseases through maximizing the potential of omics data [7,8]. Current methods for identifying DEPs can be categorized into three generations [9]: overrepresentation analysis, functional class scoring, and pathway topology. Overrepresentation analysis methods only test how significantly a pathway contains DEGs than by chance, and employ
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