Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models
Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models
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DOI：
10.1021/ac0713510
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发表时间：
2008-01-01
影响因子：
7.4
通讯作者：
Trygg, Johan
中科院分区：
文献类型：
--
作者：
Wiklund, Susanne;Johansson, Erik;Trygg, Johan
代谢组学研究产生越来越复杂的数据表，如果没有适当的工具，这些数据表很难总结和可视化。因此，使用化学计量学工具，如主成分分析（PCA）、潜在结构的偏最小二乘（PLS）和正交PLS (OPLS)，是非常重要的，因为它们包括有效的、经过验证的、强大的方法来建模信息丰富的化学和生物数据。本文提出s图作为多变量分类模型的可视化和解释工具，例如，ops判别分析，具有两个或更多类。s图显示了代谢物与模型分类之间的协方差和相关性。因此，基于对模型的贡献及其可靠性，s图有助于识别具有统计意义和潜在生物化学意义的代谢物。S-plot的扩展，即SUS-plot（共享和唯一结构），用于比较多个分类模型与共同参考（例如，控制）的结果。所使用的例子是植物生物学中基于气相色谱耦合质谱的代谢组学研究，其中两种不同的转基因杨树系与野生型进行了比较。利用ops，可以更好地显示和识别感兴趣的代谢物。
Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.