Liu Xia, Fang Yiqun, Ma Haifeng, Zhang Naixia, Li Ci
Department of Diving and Hyperbaric Medicine, Navy Medical Center, Naval Medical University (Second Military Medical University), Shanghai, 200433, China.
Shanghai University of Sport, Shanghai 200438, China.
Open Life Sci. 2023 Mar 27;18(1):20220556. doi: 10.1515/biol-2022-0556. eCollection 2023.
Unit variance (UV) scaling, mean centering (CTR) scaling, and Pareto (Par) scaling are three commonly used algorithms in the preprocessing of metabolomics data. Based on our NMR-based metabolomics studies, we found that the clustering identification performances of these three scaling methods were dramatically different as tested by the spectra data of 48 young athletes' urine samples, spleen tissue (from mice), serum (from mice), and cell (from ) samples. Our data suggested that for the extraction of clustering information, UV scaling could serve as a robust approach for NMR metabolomics data for the identification of clustering analysis even with the existence of technical errors. However, for the purpose of discriminative metabolite identification, UV scaling, CTR scaling, and Par scaling could equally extract discriminative metabolites efficiently based on the coefficient values. Based on the data presented in this study, we propose an optimal working pipeline for the selection of scaling algorithms in NMR-based metabolomics analysis, which has the potential to serve as guidance for junior researchers working in the NMR-based metabolomics research field.
单位方差（UV）缩放、均值中心化（CTR）缩放和帕累托（Par）缩放是代谢组学数据预处理中常用的三种算法。基于我们基于核磁共振的代谢组学研究，我们发现，通过对48名年轻运动员的尿液样本、脾脏组织（来自小鼠）、血清（来自小鼠）和细胞（来自）样本的光谱数据进行测试，这三种缩放方法的聚类识别性能存在显著差异。我们的数据表明，对于聚类信息的提取，即使存在技术误差，UV缩放也可以作为核磁共振代谢组学数据用于聚类分析识别的一种稳健方法。然而，为了鉴别代谢物识别的目的，UV缩放、CTR缩放和Par缩放基于系数值可以同样有效地提取鉴别代谢物。基于本研究中呈现的数据，我们提出了一种基于核磁共振的代谢组学分析中缩放算法选择的最佳工作流程，它有可能为在基于核磁共振的代谢组学研究领域工作的初级研究人员提供指导。
