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

Jemma X. Wu; Dana Pascovici; 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

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

^*Corresponding author for this work

Australian Proteome Analysis Facility (APAF)

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Statistical Analysis of Proteomic Data
Subtitle of host publication	Methods and Tools
Editors	Thomas Burger
Place of Publication	New York
Publisher	Springer, Springer Nature
Chapter	17
Pages	375-390
Number of pages	16
ISBN (Electronic)	9781071619674
ISBN (Print)	9781071619667
DOIs	https://doi.org/10.1007/978-1-0716-1967-4_17
Publication status	Published - 2023

Publication series

Name	Methods in molecular biology
Publisher	Humana Press
Volume	2426
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Keywords

Functional enrichment analysis
Gene ontology
Pathway
Proteomics
Statistical R package
WGCNA

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

Cite this

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title = "Application of WGCNA and PloGO2 in the analysis of complex proteomic data",

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",

author = "Wu, {Jemma X.} and Dana Pascovici and Walker, {Adam K.} and Mehdi Mirzaei",

year = "2023",

doi = "10.1007/978-1-0716-1967-4_17",

language = "English",

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series = "Methods in molecular biology",

publisher = "Springer, Springer Nature",

pages = "375--390",

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address = "United States",

}

Application of WGCNA and PloGO2 in the analysis of complex proteomic data. / Wu, Jemma X.; Pascovici, Dana; Walker, Adam K. et al.
Statistical Analysis of Proteomic Data: Methods and Tools. ed. / Thomas Burger. New York: Springer, Springer Nature, 2023. p. 375-390 (Methods in molecular biology; Vol. 2426).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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T1 - Application of WGCNA and PloGO2 in the analysis of complex proteomic data

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AU - Pascovici, Dana

AU - Walker, Adam K.

AU - Mirzaei, Mehdi

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Y1 - 2023

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KW - Functional enrichment analysis

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KW - Pathway

KW - Proteomics

KW - Statistical R package

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PB - Springer, Springer Nature

CY - New York

ER -

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

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Understanding the early disease mechanisms of motor neuron disease and frontotemporal dementia

Neurodegenerative disease pathology, mechanisms, models and treatments

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

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Understanding the early disease mechanisms of motor neuron disease and frontotemporal dementia

Neurodegenerative disease pathology, mechanisms, models and treatments

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