Cargando…

Local false discovery rate estimation using feature reliability in LC/MS metabolomics data

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chong, Elizabeth Y., Huang, Yijian, Wu, Hao, Ghasemzadeh, Nima, Uppal, Karan, Quyyumi, Arshed A., Jones, Dean P., Yu, Tianwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657040/
https://www.ncbi.nlm.nih.gov/pubmed/26596774
http://dx.doi.org/10.1038/srep17221
_version_ 1782402322960220160
author Chong, Elizabeth Y.
Huang, Yijian
Wu, Hao
Ghasemzadeh, Nima
Uppal, Karan
Quyyumi, Arshed A.
Jones, Dean P.
Yu, Tianwei
author_facet Chong, Elizabeth Y.
Huang, Yijian
Wu, Hao
Ghasemzadeh, Nima
Uppal, Karan
Quyyumi, Arshed A.
Jones, Dean P.
Yu, Tianwei
author_sort Chong, Elizabeth Y.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4657040
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Nature Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-46570402015-11-30 Local false discovery rate estimation using feature reliability in LC/MS metabolomics data Chong, Elizabeth Y. Huang, Yijian Wu, Hao Ghasemzadeh, Nima Uppal, Karan Quyyumi, Arshed A. Jones, Dean P. Yu, Tianwei Sci Rep Article 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. Nature Publishing Group 2015-11-24 /pmc/articles/PMC4657040/ /pubmed/26596774 http://dx.doi.org/10.1038/srep17221 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Chong, Elizabeth Y.
Huang, Yijian
Wu, Hao
Ghasemzadeh, Nima
Uppal, Karan
Quyyumi, Arshed A.
Jones, Dean P.
Yu, Tianwei
Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
title Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
title_full Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
title_fullStr Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
title_full_unstemmed Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
title_short Local false discovery rate estimation using feature reliability in LC/MS metabolomics data
title_sort local false discovery rate estimation using feature reliability in lc/ms metabolomics data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657040/
https://www.ncbi.nlm.nih.gov/pubmed/26596774
http://dx.doi.org/10.1038/srep17221
work_keys_str_mv AT chongelizabethy localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata
AT huangyijian localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata
AT wuhao localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata
AT ghasemzadehnima localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata
AT uppalkaran localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata
AT quyyumiarsheda localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata
AT jonesdeanp localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata
AT yutianwei localfalsediscoveryrateestimationusingfeaturereliabilityinlcmsmetabolomicsdata