Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
Anal Biochem. 2010 Jun 1;401(1):134-43. doi: 10.1016/j.ab.2010.02.005. Epub 2010 Feb 14.
Use of nuclear magnetic resonance (NMR)-based metabonomics to search for human disease biomarkers is becoming increasingly common. For many researchers, the ultimate goal is translation from biomarker discovery to clinical application. Studies typically involve investigators from diverse educational and training backgrounds, including physicians, academic researchers, and clinical staff. In evaluating potential biomarkers, clinicians routinely use statistical significance testing language, whereas academicians typically use multivariate statistical analysis techniques that do not perform statistical significance evaluation. In this article, we outline an approach to integrate statistical significance testing with conventional principal components analysis data representation. A decision tree algorithm is introduced to select and apply appropriate statistical tests to loadings plot data, which are then heat map color-coded according to P score, enabling direct visual assessment of statistical significance. A multiple comparisons correction must be applied to determine P scores from which reliable inferences can be made. Knowledge of means and standard deviations of statistically significant buckets enabled computation of effect sizes and study sizes for a given statistical power. Methods were demonstrated using data from a previous study. Integrated metabonomics data assessment methodology should facilitate translation of NMR-based metabonomics discovery of human disease biomarkers to clinical use.
利用基于核磁共振（NMR）的代谢组学来寻找人类疾病生物标志物的方法越来越普遍。对于许多研究人员来说，最终目标是将生物标志物的发现转化为临床应用。这些研究通常涉及来自不同教育和培训背景的研究人员，包括医生、学术研究人员和临床工作人员。在评估潜在生物标志物时，临床医生通常使用统计显著性检验语言，而学者则通常使用不进行统计显著性评估的多变量统计分析技术。在本文中，我们概述了一种将统计显著性检验与常规主成分分析数据表示集成的方法。引入了决策树算法来选择和应用适当的统计检验到载荷图数据，然后根据 P 值对热图进行颜色编码，从而可以直接直观地评估统计显著性。必须应用多重比较校正来确定可以做出可靠推断的 P 值。统计学上显著桶的均值和标准差的知识使我们能够计算给定统计功效的效应大小和研究规模。使用来自先前研究的数据演示了方法。综合代谢组学数据评估方法学应该有助于将基于 NMR 的代谢组学发现的人类疾病生物标志物转化为临床应用。
