INTRODUCTION Mass spectrometric data analysis of complex biological mixtures can be a challenge due to its vast datasets. There is lack of data treatment pipelines to analyze chemical signals versus noise. These tasks, so far, have been up to the discretion of the analysts. OBJECTIVES The aim of this work is to demonstrate an analytical workflow that would enhance the confidence in metabolomics before answering biological questions by serial dilution of botanical complex mixture and high-dimensional data analysis. Furthermore, we would like to provide an alternative approach to a univariate p-value cutoff from t-test for blank subtraction procedure between negative control and biological samples. METHODS A serial dilution of complex mixture analysis under electrospray ionization was proposed to study firsthand chemical complexity of metabolomics. Advanced statistical models using high-dimensional penalized regression were employed to study both the concentration and ion intensity relationship and the ion-ion relationship per second of retention time sub dataset. The multivariate analysis was carried out with a tool built in-house, so called metabolite ions extraction and visualization, which was implemented in R environment. RESULTS A test case of the medicinal plant goldenseal (Hydrastis canandensis L.), showed an increase in metabolome coverage of features deemed as "important" by a multivariate analysis compared to features deemed as "significant" by a univariate t-test. For an illustration, the data analysis workflow suggested an unexpected putative compound, 20-hydroxyecdysone. This suggestion was confirmed with MS/MS acquisition and literature search. CONCLUSION The multivariate analytical workflow selects "true" metabolite ions signals and provides an alternative approach to a univariate p-value cutoff from t-test, thus enhancing the data analysis process of metabolomics.

中文翻译：

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使用高维罚分回归从全电喷雾电离质谱中提取植物代谢物离子。

简介由于其庞大的数据集，对复杂的生物混合物进行质谱数据分析可能是一个挑战。缺乏用于分析化学信号与噪声的数据处理管道。到目前为止，这些任务已由分析师自行决定。目的这项工作的目的是演示一种分析流程，该流程将通过连续稀释植物复杂混合物和进行高维数据分析，在回答生物学问题之前增强对代谢组学的信心。此外，我们想为阴性对照和生物学样品之间的空白扣除程序从t检验单变量p值截止提供一种替代方法。方法提出了在电喷雾电离下对复杂混合物分析进行系列稀释的方法，以研究代谢组学的第一手化学复杂性。使用采用高维罚分回归的高级统计模型来研究浓度和离子强度关系以及保留时间子数据集每秒的离子-离子关系。多变量分析是使用内部构建的工具进行的，即所谓的代谢物离子提取和可视化，该工具在R环境中实现。结果药用植物金枪鱼（Hydrastis canandensis L.）的一个测试案例显示，与单变量t检验认为“重要”的特征相比，多变量分析认为“重要”的特征的代谢组覆盖率增加。为了举例说明，数据分析工作流程提出了一种意外的假定化合物20-羟基蜕皮激素。MS / MS采集和文献检索证实了这一建议。