Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
Bioinformatics. 2017 Jan 15;33(2):219-226. doi: 10.1093/bioinformatics/btw598. Epub 2016 Sep 15.
In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed.
Here, we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets.
The R-package "zeroSum" can be downloaded at https://github.com/rehbergT/zeroSum Moreover, we provide all R-scripts and data used to produce the results of this manuscript as Supplementary Material CONTACT: Michael.Altenbuchinger@ukr.de, Thorsten.Rehberg@ukr.de and Rainer.Spang@ukr.deSupplementary information: Supplementary material is available at Bioinformatics online.
在生物医学领域，每一个分子测量都相对于一个参考点，例如从组织中提取的固定 RNA 等分试样、一定数量的血细胞或一定体积的生物流体。参考点通常是出于实际原因选择的。例如，我们可能希望评估患病器官的代谢组学，但只能在血液或尿液中测量代谢物。在这种情况下，可观察到的数据仅间接反映疾病状态。这些参考点差异的统计影响尚未讨论。
在这里，我们表明参考点差异会影响像 LASSO 这样的回归模型的性能。作为替代方案，我们建议对参考点不敏感的分析采用零和回归。我们表明，在参考点选择不佳的情况下，零和回归在模拟和将肠道微生物组分析与代谢组学相结合的应用中均优于 LASSO。此外，我们描述了一种新的基于坐标下降的算法来拟合零和弹性网络。
R 包“zeroSum”可在 https://github.com/rehbergT/zeroSum 上下载。此外，我们还提供了产生本文结果所用的所有 R 脚本和数据作为补充材料。
Michael.Altenbuchinger@ukr.de、Thorsten.Rehberg@ukr.de 和 Rainer.Spang@ukr.de
补充材料可在 Bioinformatics 在线获得。
