Controlling the FDR in Imperfect Matches to an Incomplete Database

• 成果类型: Article
• 署名作者: Keich, Uri; Noble, William Stafford
• 署名单位: University of Sydney; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1375931
• 发表日期: 2018
• 页码: 973-982
• 关键词: false discovery rate; decoy search strategy; tandem mass-spectra; shotgun proteomics; spectrometry; rates

摘要

We consider the problem of controlling the false discovery rate (FDR) among discoveries from searching an incomplete database. This problem differs from the classical multiple testing setting because there are two different types of false discoveries: those arising from objects that have no match in the database and those that are incorrectly matched. We show that commonly used FDR controlling procedures are inadequate for this setup, a special case of which is tandem mass spectrum identification. We then derive a novel FDR controlling approach which extensive simulations suggest is unbiased. We also compare its performance with problem-specific as well as general FDR controlling procedures using both simulated and real mass spectrometry data.