Moderated Test Statistics to Detect Differential Deuteration in Hydrogen/Deuterium Exchange Mass Spectrometry Experiments,Analytical Chemistry

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Moderated Test Statistics to Detect Differential Deuteration in Hydrogen/Deuterium Exchange Mass Spectrometry Experiments
Analytical Chemistry ( IF 6.7 ) Pub Date : 2021-11-29 , DOI: 10.1021/acs.analchem.1c02346
Jürgen Claesen _{1,

2} , Srinath Krishnamurthy ₃ , Andy M Lau ₄ , Anastassios Economou ₃

Affiliation

With differential hydrogen/deuterium exchange, differences in the structure and dynamics of protein states can be studied. Detecting statistically significant differentially deuterated peptides is crucial to draw meaningful conclusions about the distinct conformations and dynamics of the protein under study. Here, we introduced a linear model in combination with an empirical Bayes approach to detect differentially deuterated peptides. Using a linear model allows one to test for differences in deuteration between two (two-sample t-test) or more groups (F-statistic), while potentially controlling for the effects of other variables that are not of interest. The empirical Bayes approach improves the estimation of deuteration-level variances, especially in experiments with a low number of replicates. As a consequence, the two sample t-tests and the F-statistic become moderated, resulting in a lower number of false positive and false negative findings. Furthermore, we introduce a thresholded-moderated t-statistic to test if the observed deuteration differences are larger than a specified, biologically relevant difference. Finally, we underline the importance of having a sufficient number of replicates, and the effect of the number of replicates on the power of the statistical significance tests. The R-code for the proposed moderated test statistics is available upon request.

中文翻译：

在氢/氘交换质谱实验中检测差异氘化的适度测试统计

通过不同的氢/氘交换，可以研究蛋白质状态的结构和动力学差异。检测具有统计学意义的差异氘化肽对于得出关于所研究蛋白质的不同构象和动力学的有意义的结论至关重要。在这里，我们引入了线性模型与经验贝叶斯方法相结合来检测差异氘化肽。使用线性模型可以测试两个（两个样本t检验）或多个组之间的氘化差异（F-statistic），同时可能控制其他不感兴趣的变量的影响。经验贝叶斯方法改进了氘化水平方差的估计，尤其是在重复次数较少的实验中。因此，两个样本t检验和F统计量变得缓和，导致假阳性和假阴性结果的数量减少。此外，我们引入了一个阈值调节的t- 用于测试观察到的氘化差异是否大于指定的生物学相关差异的统计量。最后，我们强调了具有足够重复次数的重要性，以及重复次数对统计显着性检验功效的影响。建议的适度测试统计的 R 代码可应要求提供。

更新日期：2021-12-14

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