Application of WGCNA and PloGO2 in the analysis of complex proteomic data

Jemma X. Wu*, Dana Pascovici, Adam K. Walker, Mehdi Mirzaei

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


In this protocol we describe our workflow for analyzing complex, multi-condition quantitative proteomic experiments, with the aim to extract biological insights. The tool we use is an R package, PloGO2, contributed to Bioconductor, which we can optionally precede by running correlation network analysis with WGCNA. We describe the data required and the steps we take, including detailed code examples and outputs explanation. The package was designed to generate gene ontology or pathway summaries for many data subsets at the same time, visualize protein abundance summaries for each biological category examined, help determine enriched protein subsets by comparing them all to a reference set, and suggest key highly correlated hub proteins, if the optional network analysis is employed.

Original languageEnglish
Title of host publicationStatistical Analysis of Proteomic Data
Subtitle of host publicationMethods and Tools
EditorsThomas Burger
Place of PublicationNew York
PublisherSpringer, Springer Nature
Number of pages16
ISBN (Electronic)9781071619674
ISBN (Print)9781071619667
Publication statusPublished - 2023

Publication series

NameMethods in molecular biology
PublisherHumana Press
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Functional enrichment analysis
  • Gene ontology
  • Pathway
  • Proteomics
  • Statistical R package


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