Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data

Jemma X Wu; Dana Pascovici; Yunqi Wu; Adam K Walker; Mehdi Mirzaei

doi:10.1007/978-1-0716-1967-4_17

Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data

Methods Mol Biol. 2023:2426:375-390. doi: 10.1007/978-1-0716-1967-4_17.

Authors

Jemma X Wu¹, Dana Pascovici², Yunqi Wu³, Adam K Walker⁴, Mehdi Mirzaei³

Affiliations

¹ Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW, Australia. jemma.wu@mq.edu.au.
² Insight Stats, Croydon Park, NSW, Australia.
³ Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW, Australia.
⁴ Neurodegeneration Pathobiology Laboratory Queensland Brain Institute, The University of Queensland, St. Lucia, QLD, Australia.

PMID: 36308698
DOI: 10.1007/978-1-0716-1967-4_17

Abstract

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.

Keywords: Functional enrichment analysis; Gene ontology; Pathway; Proteomics; Statistical R package; WGCNA.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Gene Ontology
Proteins*
Proteomics* / methods
Workflow

Substances

Proteins