Unilever Research and Development, Advanced Measurement and Data Modelling, P.O. Box 114, 3130 AC Vlaardingen, The Netherlands.
Anal Chim Acta. 2010 Mar 17;663(1):98-104. doi: 10.1016/j.aca.2010.01.038. Epub 2010 Jan 25.
A new strategy for biomarker discovery is presented that uses time-series metabolomics data. Data sets from samples analysed at different time points after an intervention are searched for compounds that show a meaningful trend following the intervention. Obviously, this requires new data-analytical tools to distinguish such compounds from those showing only random variation. Two univariate methods, autocorrelation and curve-fitting, are used either as stand-alone methods or in combination to discover unknown metabolites in data sets originating from target-compound analysis. Both techniques reduce the long list of detected compounds in the kinetic sample set to include only those having a pre-defined interesting time profile. Thus, new metabolites may be discovered within data structures that are usually only used for target-compound analysis. The new strategy is tested on a sample set obtained from a gut fermentation study of a polyphenol-rich diet. For this study, the initial list of over 9000 potentially interesting features was reduced to less than 150, thus significantly reducing the expensive and time-consuming manual examination.
本文提出了一种新的生物标志物发现策略，该策略使用时间序列代谢组学数据。对干预后不同时间点采集的样本进行数据分析，寻找有意义的随时间变化的化合物。显然，这需要新的数据分析工具来区分这些化合物与仅显示随机变化的化合物。本文使用两种单变量方法（自相关和曲线拟合），要么单独使用，要么组合使用，从靶向化合物分析的数据集中发现未知代谢物。这两种技术将动力学样本集中检测到的化合物的长列表减少到仅包含具有预定义有趣时间分布的化合物。因此，可能会在通常仅用于靶向化合物分析的数据结构中发现新的代谢物。该新策略在富含多酚的饮食肠道发酵研究的样本集中进行了测试。对于这项研究，将最初的 9000 多个潜在有趣特征列表减少到不到 150 个，从而大大减少了昂贵且耗时的手动检查。
