WGCNA 和 PloGO2 在复杂蛋白质组学数据分析中的应用。
Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data.
机构信息
Australian Proteome Analysis Facility, Macquarie University, Sydney, NSW, Australia.
Insight Stats, Croydon Park, NSW, Australia.
出版信息
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.
在本方案中，我们描述了分析复杂多条件定量蛋白质组学实验的工作流程，旨在提取生物学见解。我们使用的工具是一个 R 包，PloGO2，它是 Bioconductor 的贡献，我们可以选择在使用 WGCNA 进行相关网络分析之前运行它。我们描述了所需的数据和我们采取的步骤，包括详细的代码示例和输出解释。该软件包旨在同时为许多数据子集生成基因本体或途径摘要，可视化每个检查的生物学类别中的蛋白质丰度摘要，通过将所有数据与参考集进行比较来帮助确定丰富的蛋白质子集，并在使用可选的网络分析时建议关键的高相关枢纽蛋白。
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