Brenes Alejandro J
Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, United Kingdom.
J Proteome Res. 2024 Dec 6;23(12):5274-5278. doi: 10.1021/acs.jproteome.4c00461. Epub 2024 Nov 22.
The coefficient of variation (CV) is a measure that is frequently used to assess data dispersion for mass spectrometry-based proteomics. In the current era of burgeoning technical developments, there has been an increased focus on using CVs to measure the quantitative precision of new methods. Thus, it has also become important to define a set of guidelines on how to calculate and report the CVs. This perspective shows the effects that the CV equation, data normalization as well as software parameters, can have on data dispersion and CVs, highlighting the importance of reporting all these variables within the methods section. It also proposes a set of recommendations to calculate and report CVs for technical studies, where the main objective is to benchmark technical developments with a focus on precision. To assist in this process, a novel R package to calculate CVs (proteomicsCV) is also included.
变异系数（CV）是一种常用于评估基于质谱的蛋白质组学数据离散度的指标。在当前技术蓬勃发展的时代，人们越来越关注使用变异系数来衡量新方法的定量精度。因此，制定一套关于如何计算和报告变异系数的指南也变得很重要。本文观点展示了变异系数方程、数据归一化以及软件参数对数据离散度和变异系数的影响，强调了在方法部分报告所有这些变量的重要性。它还针对技术研究提出了一套计算和报告变异系数的建议，这类研究的主要目标是以精度为重点对技术发展进行基准测试。为辅助这一过程，还包含了一个用于计算变异系数的新型R包（proteomicsCV）。
