Nevin Manimala Statistics

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.


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.

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

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