Shenzhen Research Institute of Big Data, Shenzhen 518712, China.
School of Data Science, The Chinese University of Hong Kong - Shenzhen, Shenzhen 518712, China.
Bioinformatics. 2022 Jul 11;38(14):3662-3664. doi: 10.1093/bioinformatics/btac364.
Testing for pathway enrichment is an important aspect in the analysis of untargeted metabolomics data. Due to the unique characteristics of untargeted metabolomics data, some key issues have not been fully addressed in existing pathway testing algorithms: (i) matching uncertainty between data features and metabolites; (ii) lacking of method to analyze positive mode and negative mode liquid chromatography-mass spectrometry (LC/MS) data simultaneously on the same set of subjects; (iii) the incompleteness of pathways in individual software packages.
We developed an innovative R/Bioconductor package: metabolic pathway testing with positive and negative mode data (metapone), which can perform two novel statistical tests that take matching uncertainty into consideration-(i) a weighted gene set enrichment analysis-type test and (ii) a permutation-based weighted hypergeometric test. The package is capable of combining positive- and negative-ion mode results in a single testing scheme. For comprehensiveness, the built-in pathways were manually curated from three sources: Kyoto Encyclopedia of Genes and Genomes, Mummichog and The Small Molecule Pathway Database.
The package is available at https://bioconductor.org/packages/devel/bioc/html/metapone.html.
Supplementary data are available at Bioinformatics online.
通路富集测试是分析非靶向代谢组学数据的一个重要方面。由于非靶向代谢组学数据的独特特征，现有通路测试算法中仍存在一些未充分解决的关键问题：（i）数据特征与代谢物之间的匹配不确定性；（ii）缺乏同时分析同一组对象正离子模式和负离子模式液相色谱-质谱（LC/MS）数据的方法；（iii）单个软件包中通路的不完整。
我们开发了一种创新的 R/Bioconductor 包：正负离子模式数据的代谢通路测试（metapone），它可以执行两种新的统计测试，考虑到匹配不确定性：（i）加权基因集富集分析型测试和（ii）基于置换的加权超几何测试。该软件包能够在单个测试方案中结合正离子和负离子模式的结果。为了全面性，内置通路是从三个来源手动整理的：京都基因与基因组百科全书、Mummichog 和小分子通路数据库。
该软件包可在 https://bioconductor.org/packages/devel/bioc/html/metapone.html 获得。
补充数据可在 Bioinformatics 在线获得。
