Naderi Yeganeh Pourya, Richardson Chrsitine, Saule Erik, Loraine Ann, Taghi Mostafavi M
Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave., Boston, 02215 MA USA.
Department of Computer Science, The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, 28223 NC USA.
BioData Min. 2020 Jun 16;13:5. doi: 10.1186/s13040-020-00214-x. eCollection 2020.
The use of graph theory models is widespread in biological pathway analyses as it is often desired to evaluate the position of genes and proteins in their interaction networks of the biological systems. In this article, we argue that the common standard graph centrality measures do not sufficiently capture the informative topological organizations of the pathways, and thus, limit the biological inference. While key pathway elements may appear both upstream and downstream in pathways, standard directed graph centralities attribute significant topological importance to the upstream elements and evaluate the downstream elements as having no importance.We present a directed graph framework, Source/Sink Centrality (SSC), to address the limitations of standard models. SSC separately measures the importance of a node in the upstream and the downstream of a pathway, as a sender and a receiver of biological signals, and combines the two terms for evaluating the centrality. To validate SSC, we evaluate the topological position of known human cancer genes and mouse lethal genes in their respective KEGG annotated pathways and show that SSC-derived centralities provide an effective framework for associating higher positional importance to the genes with higher importance from a priori knowledge. While the presented work challenges some of the modeling assumptions in the common pathway analyses, it provides a straight-forward methodology to extend the existing models. The SSC extensions can result in more informative topological description of pathways, and thus, more informative biological inference.
图论模型在生物途径分析中广泛应用，因为人们常常希望评估基因和蛋白质在生物系统相互作用网络中的位置。在本文中，我们认为常见的标准图中心性度量方法无法充分捕捉途径中信息丰富的拓扑组织，因此限制了生物学推断。虽然关键途径元素可能在途径的上游和下游都出现，但标准有向图中心性赋予上游元素显著的拓扑重要性，而将下游元素评估为无重要性。我们提出了一个有向图框架，即源/汇中心性（SSC），以解决标准模型的局限性。SSC分别将途径上游和下游的节点作为生物信号的发送者和接收者来衡量其重要性，并将这两个项结合起来评估中心性。为了验证SSC，我们评估了已知人类癌症基因和小鼠致死基因在各自KEGG注释途径中的拓扑位置，并表明源自SSC的中心性提供了一个有效的框架，用于将更高的位置重要性与基于先验知识具有更高重要性的基因相关联。虽然本文提出的工作对常见途径分析中的一些建模假设提出了挑战，但它提供了一种直接的方法来扩展现有模型。SSC扩展可以导致对途径进行更具信息性的拓扑描述，从而进行更具信息性的生物学推断。
