PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

多数大规模蛋白质组定量方法会生成大量已定量蛋白质列表，但这类列表往往难以解读，且重现性欠佳。针对这类复杂的定量蛋白质组数据集，需借助计算方法开展分析。为此，我们提出一种统计计算方法，用于识别在一组重复实验中具有可重现定量结果的丰度蛋白质显著富集的蛋白质集合（例如基因本体（Gene Ontology，GO）条目）。基于此，我们开发了PSEA-Quant——一款适用于无标记及有标记蛋白质定量数据集的蛋白质集合富集分析算法。与GSEA、PSEA等同类方法不同，该算法为经典GO分析提供了替代方案，可对蛋白质注释偏差进行建模，并支持仅来自单一条件的样本分析。我们的研究表明，PSEA-Quant生成的结果可与GO分析形成互补。此外，通过对表达囊性纤维化跨膜传导调节因子（CFTR）突变体的细胞系开展无标记蛋白质定量分析，我们证实PSEA-Quant可提供与囊性纤维化相关生物学过程的宝贵信息。最后，基于串联质量标签（tandem mass tag，TMT）定量数据，PSEA-Quant揭示了人类、大鼠及小鼠大脑前额叶皮层中分子机制的差异。本工具已在线开放，可通过提供具有生物学意义的研究见解，提升定量蛋白质组数据集的分析质量。