Determining the significance of protein network features and attributes using permutation testing

Joseph Cursons, Melissa J. Davis

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Otherpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationProteome Bioinformatics
Place of PublicationNew York, NY
PublisherHumana Press
Chapter15
Pages199-208
Number of pages10
ISBN (Electronic)9781493967407
ISBN (Print)9781493967384
DOIs
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume1549
ISSN (Print)1064-3745

Keywords

  • Computational systems biology
  • Network structure
  • Permutation testing
  • PPI
  • PROSPERITI
  • Protein interaction
  • Proteomics

Cite this

Cursons, J., & Davis, M. J. (2017). Determining the significance of protein network features and attributes using permutation testing. In Proteome Bioinformatics (pp. 199-208). (Methods in Molecular Biology; Vol. 1549). Humana Press. https://doi.org/10.1007/978-1-4939-6740-7_15