KMS Of Academy of mathematics and systems sciences, CAS
| Transfer posterior error probability estimation for peptide identification | |
Yi,Xinpei1,2; Gong,Fuzhou1,2 ; Fu,Yan1,2
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| 2020-05-04 | |
| Source Publication | BMC Bioinformatics
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| Volume | 21Issue:1 |
| Abstract | 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. |
| Keyword | Proteomics Mass spectrometry Quality control Posterior error probability Local false discovery rate Transfer learning |
| DOI | 10.1186/s12859-020-3485-y |
| Language | 英语 |
| WOS ID | BMC:10.1186/s12859-020-3485-y |
| Publisher | BioMed Central |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/50307 |
| Collection | 应用数学研究所 |
| Corresponding Author | Gong,Fuzhou; Fu,Yan |
| Affiliation | 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 |
| Recommended Citation GB/T 7714 | 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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