ArticleScientific reports2015
Local false discovery rate estimation using feature reliability in LC/MS metabolomics data.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
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- Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study.Journal of clinical medicine · 2021Article
- Breast cancer risk in relation to plasma metabolites among Hispanic and African American women.Breast cancer research and treatment · 2019Article
- Uncovering mechanisms of global ocean change effects on the Dungeness crab (Cancer magister) through metabolomics analysis.Scientific reports · 2019Article
- Metabolic hormones and breast cancer risk among Mexican American Women in the Mano a Mano Cohort Study.Scientific reports · 2019Article
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- Beyond genomics: understanding exposotypes through metabolomics.Human genomics · 2018Review
- Clinical Relevance and Molecular Phenotypes in Gastric Cancer, of TP53 Mutations and Gene Expressions, in Combination With Other Gene Mutations.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
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