Cursons Joseph, Davis Melissa J
Systems Biology Laboratory, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia.
ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia.
Methods Mol Biol. 2017;1549:199-208. doi: 10.1007/978-1-4939-6740-7_15.
Network analysis methods are increasing in popularity. An approach commonly applied to analyze proteomics data involves the use of protein-protein interaction (PPI) networks to explore the systems-level cooperation between proteins identified in a study. In this context, protein interaction networks can be used alongside the statistical analysis of proteomics data and traditional functional enrichment or pathway enrichment analyses. In network analysis it is possible to adjust for some of the complexities that arise due to the known, explicit interdependence between the measured quantities, in particular, differences in the number of interactions between proteins. Here we describe a method for calculating robust empirical p-values for protein interaction networks. We also provide a worked example with python code demonstrating the implementation of this methodology.
网络分析方法越来越受欢迎。一种常用于分析蛋白质组学数据的方法是使用蛋白质-蛋白质相互作用(PPI)网络来探索研究中鉴定出的蛋白质之间的系统级协作。在这种情况下,蛋白质相互作用网络可与蛋白质组学数据的统计分析以及传统的功能富集或通路富集分析一起使用。在网络分析中,可以针对由于测量量之间已知的、明确的相互依赖性而产生的一些复杂性进行调整,特别是蛋白质之间相互作用数量的差异。在这里,我们描述一种计算蛋白质相互作用网络稳健经验p值的方法。我们还提供了一个带有Python代码的实例,展示了该方法的实现。