title: Local false discovery rate estimation using feature reliability in LC/MS metabolomics data.
abstract: False discovery rate (FDR) control is an important tool of statistical inference in feature selection. In mass spectrometry-based metabolomics data, features can be measured at different levels of reliability and false features are often detected in untargeted metabolite profiling as chemical and/or bioinformatics noise. The traditional false discovery rate methods treat all features equally, which can cause substantial loss of statistical power to detect differentially expressed features. We propose a reliability index for mass spectrometry-based metabolomics data with repeated measurements, which is quantified using a composite measure. We then present a new method to estimate the local false discovery rate (lfdr) that incorporates feature reliability. In simulations, our proposed method achieved better balance between sensitivity and controlling false discovery, as compared to traditional lfdr estimation. We applied our method to a real metabolomics dataset and were able to detect more differentially expressed metabolites that were biologically meaningful.
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Elizabeth Y
authors.initials: EY
authors.lastname: Chong
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Yijian
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Hao
authors.initials: H
authors.lastname: Wu
authors.affiliation: Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Nima
authors.initials: N
authors.lastname: Ghasemzadeh
authors.affiliation: Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Karan
authors.initials: K
authors.lastname: Uppal
authors.affiliation: Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Arshed A
authors.initials: AA
authors.lastname: Quyyumi
authors.affiliation: Department of Medicine, School of Medicine, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Dean P
authors.initials: DP
authors.lastname: Jones
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA, 30322.
authors.forename: Tianwei
authors.initials: T
authors.lastname: Yu
mesh_terms: D015415:Biomarkers / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood; D002853:Chromatography, Liquid; D003324:Coronary Artery Disease / Q000097:blood / Q000175:diagnosis; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D006801:Humans; D013058:Mass Spectrometry; D055432:Metabolomics / Q000379:methods*
chemical_list: D015415:Biomarkers; D008076:Cholesterol, HDL