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.
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Acknowledgements
This work was conducted at the Australian Proteome Analysis Facility supported by the Australian Government’s National Collaborative Research Infrastructure Scheme (NCRIS). Aspects of this work were supported by the Australian National Health and Medical Research Council (Project Grant GNT1124005 and RD Wright Career Development Fellowship GNT1140386 to AKW), the Ross Maclean Fellowship, and the Brazil Family Program for Neurology.
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Wu, J.X., Pascovici, D., Wu, Y., Walker, A.K., Mirzaei, M. (2023). Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data. In: Burger, T. (eds) Statistical Analysis of Proteomic Data. Methods in Molecular Biology, vol 2426. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1967-4_17
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DOI: https://doi.org/10.1007/978-1-0716-1967-4_17
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