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

SUBMITTER: Chong EY 

PROVIDER: S-EPMC4657040 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature