MOE Key Laboratory of Biosystems Homeostasis & Protection, and Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang 310030, China.
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, China.
Brief Bioinform. 2024 Mar 27;25(3). doi: 10.1093/bib/bbae116.
Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.
富集分析将生物学特征置于通路中，有助于系统地理解高维数据，在生物医学研究中得到了广泛应用。新兴的基于报道基因评分的分析 (RSA) 方法显示出更高的灵敏度，因为它依赖于 P 值而不是特征的原始值。然而，由于缺乏适当的工具，RSA 不能直接应用于多组和纵向实验设计，并且经常被错误使用。在这里，我们提出了用于多组和纵向组学数据的广义报道基因评分分析（GRSA）方法。与其他流行的富集分析方法的比较表明，GRSA 在多个基准数据集上都具有更高的灵敏度。我们将 GRSA 应用于微生物组、转录组和代谢组数据，并在组学研究中发现了新的生物学见解。最后，我们通过使用分类数据库证明了 GRSA 在功能富集之外的应用。我们在 R 包 ReporterScore 中实现了 GRSA，该包与强大的可视化模块和可更新的通路数据库集成在一起，可在 Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore) 上获得。我们相信 ReporterScore 包将成为广泛的生物医学研究领域的宝贵资产。
