Linear Discriminant Analysis - Based Estimation of the False Discovery Rate for Phosphopeptide Identifications
This paper describes a method to estimate the False Discovery Rate (FDR) of phosphopeptide identifications. The method starts with a re-assignment of the phosphorylation site/sites to those phosphopeptides for which there exists an ambiguity in the original assignment of the phosphorylation site/sites. It then performs an online data training using Expectation Maximization to estimate the joint distribution underlying the observed search results of multiple parameters from search engines. A Linear Discriminant Analysis (LDA) is subsequently carried out to optimally combine the search results into a discriminant score that possesses the most discriminating power. Based on the discriminant score, the p-value and q-value (explain a little bit more) for each identified phosphopeptide are calculated and the FDR for the set of phosphopeptides which are claimed correct identifications can then be rigorously estimated based on its definition. The approach can be easily extended to estimate the FDR of unmodified peptides. The proposed approach has been applied to datasets from a study of the effect of high-dose radiation on human skin fibroblast cells. The data analysis procedure has been coded into a software package which is freely available.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 935058
- Report Number(s):
- PNNL-SA-56421
- Journal Information:
- Journal of Proteome Research, 7(6):2195-2203, Vol. 7, Issue 6
- Country of Publication:
- United States
- Language:
- English
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