Lin Weiqiang, Ji Jiadong, Zhu Yuchen, Li Mingzhuo, Zhao Jinghua, Xue Fuzhong, Yuan Zhongshang
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Department of Data Science, School of Statistics, Shandong University of Finance and Economics, Jinan, China.
Front Genet. 2020 Oct 15;11:556259. doi: 10.3389/fgene.2020.556259. eCollection 2020.
Complex diseases are believed to be the consequence of intracellular network(s) involving a range of factors. An improved understanding of a disease-predisposing biological network could lead to better identification of genes and pathways that confer disease risk and therefore inform drug development. The group difference in biological networks, as is often characterized by graphs of nodes and edges, is attributable to effects of these nodes and edges. Here we introduced pointwise mutual information (PMI) as a measure of the connection between a pair of nodes with either a linear relationship or nonlinear dependence. We then proposed a PMI-based network regression (PMINR) model to differentiate patterns of network changes (in node or edge) linking a disease outcome. Through simulation studies with various sample sizes and inter-node correlation structures, we showed that PMINR can accurately identify these changes with higher power than current methods and be robust to the network topology. Finally, we illustrated, with publicly available data on lung cancer and gene methylation data on aging and Alzheimer's disease, an evaluation of the practical performance of PMINR. We concluded that PMI is able to capture the generic inter-node correlation pattern in biological networks, and PMINR is a powerful and efficient approach for biological network analysis.
复杂疾病被认为是涉及一系列因素的细胞内网络的结果。对导致疾病的生物网络有更深入的了解，可能会更好地识别赋予疾病风险的基因和途径，从而为药物开发提供信息。生物网络中的组间差异，通常以节点和边的图来表征，可归因于这些节点和边的效应。在这里，我们引入逐点互信息（PMI）作为衡量具有线性关系或非线性依赖关系的一对节点之间连接的指标。然后，我们提出了一种基于PMI的网络回归（PMINR）模型，以区分连接疾病结局的网络变化（节点或边）模式。通过对各种样本量和节点间相关结构的模拟研究，我们表明PMINR能够以比当前方法更高的效能准确识别这些变化，并且对网络拓扑具有鲁棒性。最后，我们利用公开可用的肺癌数据以及衰老和阿尔茨海默病的基因甲基化数据，对PMINR的实际性能进行了评估。我们得出结论，PMI能够捕捉生物网络中一般的节点间相关模式，并且PMINR是一种用于生物网络分析的强大而有效的方法。
