Principal Component Analysis (PCA) Loading and Statistical Tests for Nuclear Magnetic Resonance (NMR) Metabolomics Involving Multiple Study Groups
Principal Component Analysis (PCA) Loading and Statistical Tests for Nuclear Magnetic Resonance (NMR) Metabolomics Involving Multiple Study Groups
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DOI：
10.1080/00032719.2021.2019758
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发表时间：
2021-12-19
影响因子：
2
通讯作者：
Wang, Bo
中科院分区：
文献类型：
--
作者：
Jiang, Lin;Sullivan, Hunter;Wang, Bo
代谢组学是一个跨学科领域，整合了仪器，数据科学和生物化学的知识。代谢组学研究的是使用分析平台进行各种处理后大量代谢物的变化。然而，解释方法还没有得到充分的研究。主成分分析（PCA）是一种描述高通量代谢物数据的无监督方法，其不同于经常存在过拟合问题的监督方法，例如偏最小二乘判别分析（PLS-DA）。对于代谢组学而言，PCA负荷的解释，特别是对于多个研究组的研究，还没有得到很好的发展。在这项研究中，一种新的方法，整合PCA负荷值与常用的统计t检验分析，显着提高解释的方便性和效率。该方法被证明是使用实际的研究，从海葵提取物的NMR代谢组学处理与6阿特拉津浓度。结果表明，该方法适用于早期发现的多组代谢组学，如低浓度和潜在的纵向研究。总之，这种方法可能是至关重要的研究，如环境代谢与各种刺激因素的数据解释是以前不完全开发。
Metabolomics is an interdisciplinary area that integrates knowledge of instrumentation, data science, and biochemistry. Metabolomics studies the changes in a large number of metabolites after various treatments using analytical platforms. However, the interpretation approaches have not been completely investigated. Principal component analysis (PCA) is an unsupervised method that describes high throughput metabolite data, which is different from supervised approaches such as partial least-squares discriminant analysis (PLS-DA) which frequently has overfitting problems. The interpretation of PCA loadings, especially for studies with multiple study groups, is not well developed for metabolomics. In this study, a new method was reported that integrates PCA loading values with the commonly used statistical t-test analysis to significantly improve the convenience and efficiency of interpretation. The method was demonstrated using practical studies of NMR metabolomics on the extracts from sea anemone that were treated with six atrazine concentrations. The results indicated that the approach is suitable for multiple groups of metabolomics for early-stage discoveries, such as low concentrations and potentially longitudinal studies. In summary, this methodology may be critical in studies such as environmental metabolomics with various stimuli factors where the data interpretation was previously incompletely developed.