计算和报告基于 DIA 的蛋白质组学的变异系数。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Alejandro J Brenes
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引用次数: 0

摘要

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分散性的指标。在当前技术飞速发展的时代,人们越来越重视使用变异系数来衡量新方法的定量精度。因此,确定一套如何计算和报告 CVs 的指南也变得非常重要。本视角展示了 CV 方程、数据归一化以及软件参数对数据离散度和 CV 的影响,强调了在方法部分报告所有这些变量的重要性。报告还提出了一套建议,用于计算和报告技术研究的 CV 值,其主要目的是以精度为重点,为技术发展提供基准。为了协助这一过程,还包括一个计算 CV 的新型 R 软件包(proteomicsCV)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics.

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.

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来源期刊
Journal of Proteome Research
Journal of Proteome Research 生物-生化研究方法
CiteScore
9.00
自引率
4.50%
发文量
251
审稿时长
3 months
期刊介绍: Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".
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