Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Metabolomics data is typically scaled
to a common reference like
a constant volume of body fluid, a constant creatinine level, or a
constant area under the spectrum. Such scaling of the data, however,
may affect the selection of biomarkers and the biological interpretation
of results in unforeseen ways. Here, we studied how both the outcome
of hypothesis tests for differential metabolite concentration and
the screening for multivariate metabolite signatures are affected
by the choice of scale. To overcome this problem for metabolite signatures
and to establish a scale-invariant biomarker discovery algorithm,
we extended linear zero-sum regression to the logistic regression
framework and showed in two applications to 1H NMR-based
metabolomics data how this approach overcomes the scaling problem.
Logistic zero-sum regression is available as an R package as well
as a high-performance computing implementation that can be downloaded
at https://github.com/rehbergT/zeroSum.

代谢组学数据通常需基于统一参考基准进行缩放处理，例如以恒定体积的体液、恒定肌酐水平或光谱下恒定面积作为参考。然而，此类数据缩放操作可能以不可预见的方式影响生物标志物（biomarker）的筛选以及研究结果的生物学阐释。本研究探讨了缩放方式的选择会如何同时影响差异代谢物浓度的假设检验结果，以及多变量代谢特征的筛选流程。为解决代谢特征筛选中的上述问题，并构建具备尺度不变性的生物标志物发现算法，我们将线性零和回归（linear zero-sum regression）拓展至逻辑回归（logistic regression）框架，并通过两项基于1H NMR的代谢组学数据应用案例，证实了该方法可有效克服缩放带来的问题。逻辑零和回归可通过R包以及高性能计算实现两种形式获取，相关资源可从https://github.com/rehbergT/zeroSum下载。