We describe a useful workflow for characterizing proteomics experiments incorporating many conditions and abundance data using the popular weighted gene correlation network analysis (WGCNA) approach and functional annotation with the PloGO2 R package, the latter of which we have extended and made available to Bioconductor. The approach can use quantitative data from labeled or label-free experiments and was developed to handle multiple files stemming from data partition or multiple pairwise comparisons. The WGCNA approach can similarly produce a potentially large number of clusters of interest, which can also be functionally characterized using PloGO2. Enrichment analysis will identify clusters or subsets of proteins of interest, and the WGCNA network topology scores will produce a ranking of proteins within these clusters or subsets. This can naturally lead to prioritized proteins to be considered for further analysis or as candidates of interest for validation in the context of complex experiments. We demonstrate the use of the package on two published data sets using two different biological systems (plant and human plasma) and proteomics platforms (sequential window acquisition of all theoretical fragment-ion spectra (SWATH) and tandem mass tag (TMT)): an analysis of the effect of drought on rice over time generated using TMT and a pediatric plasma sample data set generated using SWATH. In both, the automated workflow recapitulates key insights or observations of the published papers and provides additional suggestions for further investigation. These findings indicate that the data set analysis using WGCNA combined with the updated PloGO2 package is a powerful method to gain biological insights from complex multifaceted proteomics experiments.

中文翻译：

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使用WGCNA和PloGO2从复杂的多条件蛋白质组学实验中快速提取生物学见解的工作流程。

我们描述了一种有用的工作流程，用于使用流行的加权基因相关网络分析（WGCNA）方法和带有PloGO2 R软件包的功能注释来表征包含许多条件和丰度数据的蛋白质组学实验，我们已经扩展了后者并将其提供给Bioconductor。该方法可以使用来自标记或无标记实验的定量数据，并且被开发用于处理源自数据分区或多个成对比较的多个文件。WGCNA方法可以类似地产生潜在的大量关注簇，这些簇也可以使用PloGO2进行功能表征。富集分析将确定目标蛋白质的簇或子集，并且WGCNA网络拓扑评分将对这些簇或子集内的蛋白质进行排名。这自然可以导致优先考虑的蛋白质被考虑用于进一步分析，或者被认为是复杂实验中验证的目标候选物。我们展示了使用两个不同的生物系统（植物和人类血浆）和蛋白质组学平台（所有理论碎片离子光谱（SWATH）和串联质谱标签（TMT）的顺序窗口采集）的两个已公开数据集上的包装使用： TMT生成的干旱对水稻随时间变化的影响分析和SWATH生成的小儿血浆样本数据集。在这两种方法中，自动化工作流概括了已发表论文的关键见解或观察结果，并提供了进一步研究的其他建议。