Australian Proteome Analysis Facility, Macquarie University, Sydney, New South Wales 2109, Australia.
Neurodegeneration Pathobiology Laboratory, Queensland Brain Institute, The University of Queensland, St. Lucia, Queensland 4072, Australia.
J Proteome Res. 2020 Jul 2;19(7):2898-2906. doi: 10.1021/acs.jproteome.0c00198. Epub 2020 May 22.
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.
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 PloGO2 R 包进行功能注释来描述包含许多条件和丰度数据的蛋白质组学实验，后者我们已经扩展并提供给 Bioconductor。该方法可以使用标记或无标记实验的定量数据，并开发用于处理源自数据分区或多个两两比较的多个文件。WGCNA 方法同样可以产生大量感兴趣的聚类，也可以使用 PloGO2 对其进行功能描述。富集分析将识别感兴趣的蛋白质聚类或子集，并且 WGCNA 网络拓扑评分将在这些聚类或子集中产生蛋白质的排序。这自然会导致优先考虑的蛋白质，以便进一步分析或作为复杂实验背景下感兴趣的验证候选者。我们使用两个不同的生物学系统（植物和人类血浆）和蛋白质组学平台（顺序窗口采集所有理论片段离子谱（SWATH）和串联质量标签（TMT））上的两个已发表数据集演示了该软件包的使用：使用 TMT 生成的随时间推移对水稻干旱影响的分析和使用 SWATH 生成的儿科血浆样本数据集。在这两种情况下，自动化工作流程都再现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额外建议。这些发现表明，使用 WGCNA 结合更新的 PloGO2 包对数据集进行分析是从复杂的多方面蛋白质组学实验中获得生物学见解的有力方法。
