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Filtering procedures for untargeted LC-MS metabolomics data
BACKGROUND: Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to inves...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6570933/ https://www.ncbi.nlm.nih.gov/pubmed/31200644 http://dx.doi.org/10.1186/s12859-019-2871-9 |
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author | Schiffman, Courtney Petrick, Lauren Perttula, Kelsi Yano, Yukiko Carlsson, Henrik Whitehead, Todd Metayer, Catherine Hayes, Josie Rappaport, Stephen Dudoit, Sandrine |
author_facet | Schiffman, Courtney Petrick, Lauren Perttula, Kelsi Yano, Yukiko Carlsson, Henrik Whitehead, Todd Metayer, Catherine Hayes, Josie Rappaport, Stephen Dudoit, Sandrine |
author_sort | Schiffman, Courtney |
collection | PubMed |
description | BACKGROUND: Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. RESULTS: Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering. CONCLUSIONS: Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2871-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6570933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65709332019-06-20 Filtering procedures for untargeted LC-MS metabolomics data Schiffman, Courtney Petrick, Lauren Perttula, Kelsi Yano, Yukiko Carlsson, Henrik Whitehead, Todd Metayer, Catherine Hayes, Josie Rappaport, Stephen Dudoit, Sandrine BMC Bioinformatics Methodology Article BACKGROUND: Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. RESULTS: Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering. CONCLUSIONS: Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2871-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-14 /pmc/articles/PMC6570933/ /pubmed/31200644 http://dx.doi.org/10.1186/s12859-019-2871-9 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Schiffman, Courtney Petrick, Lauren Perttula, Kelsi Yano, Yukiko Carlsson, Henrik Whitehead, Todd Metayer, Catherine Hayes, Josie Rappaport, Stephen Dudoit, Sandrine Filtering procedures for untargeted LC-MS metabolomics data |
title | Filtering procedures for untargeted LC-MS metabolomics data |
title_full | Filtering procedures for untargeted LC-MS metabolomics data |
title_fullStr | Filtering procedures for untargeted LC-MS metabolomics data |
title_full_unstemmed | Filtering procedures for untargeted LC-MS metabolomics data |
title_short | Filtering procedures for untargeted LC-MS metabolomics data |
title_sort | filtering procedures for untargeted lc-ms metabolomics data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6570933/ https://www.ncbi.nlm.nih.gov/pubmed/31200644 http://dx.doi.org/10.1186/s12859-019-2871-9 |
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