Multivariate Analysis in Metabolomics.
Multivariate Analysis in Metabolomics.
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DOI：
10.2174/2213235x11301010092
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Powers R
中科院分区：
文献类型：
--
作者：
Worley B;Powers R
代谢组学旨在提供细胞和生物液体中所有小分子代谢物的全球快照，没有更集中的代谢研究所固有的观察偏差。然而，这种全球分析的惊人的高信息含量引入了其自身的挑战;从任何给定的代谢组学数据集有效地形成生物相关的结论确实需要专门形式的数据分析。在代谢组学数据集中发现意义的一种方法涉及多变量分析（MVA）方法，例如主成分分析（PCA）和对潜在结构的偏最小二乘投影（PLS），其中识别对变化或分离贡献最大的光谱特征以用于进一步分析。然而，与任何数学处理，这些方法并不是万能的;这篇综述讨论了使用多变量分析代谢组学，以及常见的陷阱和误解。
Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.