Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria.
Department of Analytical Chemistry, Institute of Chemistry, University of Silesia, Katowice, Poland.
J Chromatogr A. 2014 Oct 3;1362:194-205. doi: 10.1016/j.chroma.2014.08.050. Epub 2014 Aug 21.
Our study focuses on the removal of the so-called size effect, related to a different sample volume and/or concentration. This effect is associated with many types of instrumental signals, particularly with those originating from HPLC-DAD, LC-MS, and UPLC-MS. These signals do not carry any absolute information about the sample components. If the data comparison has to be performed based on sample fingerprints, then the size effect is undesired, and the shape effect is of main interest. With "shape", we refer to data information which is contained in the ratios between the variables. So far, different normalization methods have been applied to the removal of size effect. In our study, the performance of popular normalization methods is compared with those of the CODA (Compositional Data Analysis) methods, relying on log-ratio transformations, and the performance is evaluated through the prism of proper identification of biomarkers.
我们的研究侧重于消除所谓的“尺寸效应”，该效应与不同的样品体积和/或浓度有关。这种效应与许多类型的仪器信号有关，特别是与来自 HPLC-DAD、LC-MS 和 UPLC-MS 的信号有关。这些信号不携带关于样品成分的任何绝对信息。如果必须基于样品指纹进行数据比较，则不希望存在尺寸效应，而形状效应是主要关注点。通过“形状”，我们指的是包含在变量之间的比值中的数据信息。到目前为止，已经应用了不同的归一化方法来消除尺寸效应。在我们的研究中，比较了流行的归一化方法与基于对数比变换的 CODA（组合数据分析）方法的性能，并通过适当识别生物标志物的角度评估了性能。
