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Detecting differential protein abundance by combining peptide level P-values.
Molecular Omics ( IF 2.9 ) Pub Date : 2020-09-11 , DOI: 10.1039/d0mo00045k
Bryan J Killinger 1 , Vladislav A Petyuk , Aaron T Wright
Affiliation  

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

中文翻译:

通过结合肽水平 P 值检测差异蛋白质丰度。

在无标记 LC-MS 自下而上的蛋白质组学实验中检测样品之间差异丰富蛋白质的大多数方法依赖于统计测试从肽电离强度或平均肽水平统计数据推导出的蛋白质丰度。在这里,我们直接统计测试肽电离强度,并使用经验布朗方法 (EBM)组合产生的相关P值,避免通过估计蛋白质丰度或总结测试统计数据引入的错误。我们表明,在一个掺入蛋白质组学数据集上,使用 EBM 的肽水平方法优于使用蛋白质水平方法和几个分析工作流程(包括MSstats )的差异丰度检测. 此外,我们通过从基于活动的蛋白质分析数据集中检测富集蛋白质来证明这种方法的有效性。
更新日期:2020-11-03
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