Computational Life Science Cluster (CLiC), KBC, Umeå University, S-901 87, Umeå, Sweden.
Anal Chim Acta. 2010 Feb 5;659(1-2):23-33. doi: 10.1016/j.aca.2009.11.042. Epub 2009 Nov 22.
Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.
代谢组学是一个后基因组研究领域，致力于开发分析生物系统（如细胞、器官或生物体）中低分子量化合物的方法。分析未受干扰和受干扰系统（如健康志愿者和患有疾病的患者）之间的代谢差异，可以深入了解潜在的病理。在代谢组学分析中，为了对样本进行特征描述，通常会生成大量的数据。因此，通常采用多元数据分析技术和化学计量学来获得可靠的结果。代谢组学已应用于不同领域，如疾病诊断、毒理学、植物科学以及药物和环境研究。在这篇综述中，我们将更详细地研究疾病诊断领域中使用的化学计量学方法和现有结果。我们将首先介绍一些当前用于进行代谢组学研究的策略，特别是针对疾病诊断的策略。重点将放在数据分析策略和多元模型的验证上，因为在这方面存在很多陷阱。此外，我们还强调了最有趣的代谢组学出版物，并对其进行了详细讨论；另外还提到了一些其他研究作为感兴趣的读者的参考。一个普遍的趋势是越来越注重生物学解释，而不仅仅是对样本进行分类的能力。在结论中，我们提供了一般趋势和一些关于改进代谢组学数据分析的建议。
