Abstract
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.
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Acknowledgment
M.J.D. is supported by the Australian NBCF CDF-14-043. J.C. is funded by the Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology (project number CE140100036). We thank colleagues within the Systems Biology Laboratory at the University of Melbourne and the Bioinformatics Division at the Walter and Eliza Hall Institute for constructive suggestions and useful discussions around this topic.
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Cursons, J., Davis, M.J. (2017). Determining the Significance of Protein Network Features and Attributes Using Permutation Testing. In: Keerthikumar, S., Mathivanan, S. (eds) Proteome Bioinformatics. Methods in Molecular Biology, vol 1549. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6740-7_15
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DOI: https://doi.org/10.1007/978-1-4939-6740-7_15
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