Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS datas -: A potential tool for multi-parametric diagnosis
Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS datas -: A potential tool for multi-parametric diagnosis
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DOI：
10.1021/pr0600071
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发表时间：
2006-06-01
影响因子：
4.4
通讯作者：
Antti, Henrik
中科院分区：
文献类型：
--
作者：
Jonsson, Par;Johansson, Elin Sjovik;Antti, Henrik
提出了一种基于GC-MS数据分辨率和多变量数据分析的预测代谢物谱分析方法，并将其应用于三种不同的生物流体数据集（大鼠尿液、白杨叶提取物和人血浆）。使用分层多变量曲线解析（H-MCR）将GC-MS数据同时解析为纯曲线，描述样品之间的相对代谢物浓度，用于多变量分析。在这里，我们提出了一个扩展的H-MCR方法，允许治疗的独立样本根据处理参数估计从一组训练样本。然后可以进行基于其代谢物谱的新样品的预测或将其纳入现有模型中，这是在以下范围内的工作应用的要求：例如，在一个实施例中，临床诊断除了允许独立样品的处理和预测之外，所提出的方法还减少了曲线解析过程的时间，因为只有代表性样品的子集必须被处理，而剩余的样品可以根据所获得的处理参数来处理。在大鼠尿液示例中，解析30个训练样品所需的时间约为13小时，而根据训练参数处理30个测试样品仅需要每个样品约30秒（总共约为15分钟）。此外，所呈现的结果表明，所建议的方法适用于描述不同生物流体中的代谢变化，表明这是一种用于高通量预测代谢物谱的通用方法，该方法可能在植物功能基因组学、药物毒性、治疗功效和早期疾病诊断等领域具有重要应用。
A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e. g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample ( similar to 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.