Department of Computational Biology , University of Lausanne , 1015 Lausanne , Switzerland.
Swiss Institute of Bioinformatics , 1015 Lausanne , Switzerland.
J Proteome Res. 2019 Sep 6;18(9):3360-3368. doi: 10.1021/acs.jproteome.9b00295. Epub 2019 Aug 1.
Identification of metabolites in large-scale H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we tested the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of covarying features derived from principal component analysis (PCA), the iterative signature algorithm (ISA), and averaged correlation profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. On the basis of the number and quality of the matches, we concluded that ISA and ACP can robustly identify ten and nine metabolites, respectively, half of which were shared, while PCA did not produce any signatures with robust matches.
由于谱图的复杂性及其对 pH 值和离子浓度的敏感性，从人体生物流体的大型 1H NMR 数据中鉴定代谢物仍然具有挑战性。在这项工作中，我们测试了三种分析工具从 968 个人类尿液样本的 NMR 图谱中提取代谢物特征的能力。具体来说，我们研究了源于主成分分析 (PCA)、迭代特征算法 (ISA) 和平均相关图谱 (ACP) 的协变特征集，ACP 是我们受 STOCSY 方法启发而设计的一种新方法。我们使用先前开发的代谢物匹配方法将这些算法生成的集合与公共数据库中可获得的单个代谢物的 NMR 光谱进行匹配。根据匹配的数量和质量，我们得出结论，ISA 和 ACP 可以分别稳健地识别十种和九种代谢物，其中一半是共享的，而 PCA 则没有产生任何具有稳健匹配的特征。
