Lötsch Jörn, Kringel Dario, Ultsch Alfred
Institute of Clinical Pharmacology, Goethe University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany.
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.
Biomedicines. 2024 Jul 23;12(8):1639. doi: 10.3390/biomedicines12081639.
Fold change is a common metric in biomedical research for quantifying group differences in omics variables. However, inconsistent calculation methods and inadequate reporting lead to discrepancies in results. This study evaluated various fold-change calculation methods aiming at a recommendation of a preferred approach. The primary distinction in fold-change calculations lies in defining group expected values for log ratio computation. To challenge method interchangeability in a "stress test" scenario, we generated diverse artificial data sets with varying distributions (identity, uniform, normal, log-normal, and a mixture of these) and compared calculated fold-changes to known values. Additionally, we analyzed a multi-omics biomedical data set to estimate to what extent the findings apply to real-world data. Using arithmetic means as expected values for treatment and reference groups yielded inaccurate fold-change values more frequently than other methods, particularly when subgroup distributions and/or standard deviations differed significantly. The arithmetic mean method, often perceived as standard or picked without considering alternatives, is inferior to other definitions of the group expected value. Methods using median, geometric mean, or paired fold-change combinations are more robust against violations of equal variances or dissimilar group distributions. Adhering to methods less sensitive to data distribution without trade-offs and accurately reporting calculation methods in scientific reports is a reasonable practice to ensure correct interpretation and reproducibility.
倍数变化是生物医学研究中用于量化组学变量组间差异的常用指标。然而，计算方法不一致和报告不充分导致结果存在差异。本研究评估了各种倍数变化计算方法，旨在推荐一种首选方法。倍数变化计算的主要区别在于定义用于对数比率计算的组期望值。为了在“压力测试”场景中挑战方法的互换性，我们生成了具有不同分布（恒等分布、均匀分布、正态分布、对数正态分布以及这些分布的混合）的各种人工数据集，并将计算出的倍数变化与已知值进行比较。此外，我们分析了一个多组学生物医学数据集，以估计这些发现适用于实际数据的程度。与其他方法相比，使用算术平均值作为处理组和参照组的期望值更频繁地产生不准确的倍数变化值，特别是当亚组分布和/或标准差存在显著差异时。算术平均法通常被视为标准方法，或者在未考虑其他方法的情况下被采用，它不如组期望值的其他定义。使用中位数、几何平均值或配对倍数变化组合的方法在违反方差齐性或组分布不同时更稳健。坚持使用对数据分布不太敏感的方法且不做权衡，并在科学报告中准确报告计算方法，是确保正确解释和可重复性的合理做法。
