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Transfer posterior error probability estimation for peptide identification
Yi,Xinpei1,2; Gong,Fuzhou1,2; Fu,Yan1,2
2020-05-04
发表期刊BMC Bioinformatics
卷号21期号:1
摘要AbstractBackgroundIn shotgun proteomics, database searching of tandem mass spectra results in a great number of peptide-spectrum matches (PSMs), many of which are false positives. Quality control of PSMs is a multiple hypothesis testing problem, and the false discovery rate (FDR) or the posterior error probability (PEP) is the commonly used statistical confidence measure. PEP, also called local FDR, can evaluate the confidence of individual PSMs and thus is more desirable than FDR, which evaluates the global confidence of a collection of PSMs. Estimation of PEP can be achieved by decomposing the null and alternative distributions of PSM scores as long as the given data is sufficient. However, in many proteomic studies, only a group (subset) of PSMs, e.g. those with specific post-translational modifications, are of interest. The group can be very small, making the direct PEP estimation by the group data inaccurate, especially for the high-score area where the score threshold is taken. Using the whole set of PSMs to estimate the group PEP is inappropriate either, because the null and/or alternative distributions of the group can be very different from those of combined scores.ResultsThe transfer PEP algorithm is proposed to more accurately estimate the PEPs of peptide identifications in small groups. Transfer PEP derives the group null distribution through its empirical relationship with the combined null distribution, and estimates the group alternative distribution, as well as the null proportion, using an iterative semi-parametric method. Validated on both simulated data and real proteomic data, transfer PEP showed remarkably higher accuracy than the direct combined and separate PEP estimation methods.ConclusionsWe presented a novel approach to group PEP estimation for small groups and implemented it for the peptide identification problem in proteomics. The methodology of the approach is in principle applicable to the small-group PEP estimation problems in other fields.
关键词Proteomics Mass spectrometry Quality control Posterior error probability Local false discovery rate Transfer learning
DOI10.1186/s12859-020-3485-y
语种英语
WOS记录号BMC:10.1186/s12859-020-3485-y
出版者BioMed Central
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/50307
专题应用数学研究所
通讯作者Gong,Fuzhou; Fu,Yan
作者单位1.National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
2.School of Mathematical Sciences, University of Chinese Academy of Sciences
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Yi,Xinpei,Gong,Fuzhou,Fu,Yan. Transfer posterior error probability estimation for peptide identification[J]. BMC Bioinformatics,2020,21(1).
APA Yi,Xinpei,Gong,Fuzhou,&Fu,Yan.(2020).Transfer posterior error probability estimation for peptide identification.BMC Bioinformatics,21(1).
MLA Yi,Xinpei,et al."Transfer posterior error probability estimation for peptide identification".BMC Bioinformatics 21.1(2020).
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