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New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis
INTRODUCTION: Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. However, spectral artifacts can generate large numbers of spectral features (peaks) that do not correspond to k...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6153687/ https://www.ncbi.nlm.nih.gov/pubmed/30830442 http://dx.doi.org/10.1007/s11306-018-1426-9 |
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author | Mitchell, Joshua M. Flight, Robert M. Wang, Qing Jun Higashi, Richard M. Fan, Teresa W.-M. Lane, Andrew N. Moseley, Hunter N. B. |
author_facet | Mitchell, Joshua M. Flight, Robert M. Wang, Qing Jun Higashi, Richard M. Fan, Teresa W.-M. Lane, Andrew N. Moseley, Hunter N. B. |
author_sort | Mitchell, Joshua M. |
collection | PubMed |
description | INTRODUCTION: Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. However, spectral artifacts can generate large numbers of spectral features (peaks) that do not correspond to known compounds. Misassignment of these artifactual features creates interpretive errors and limits our ability to discern the role of representative features within living systems. OBJECTIVES: Our goal is to develop rigorous methods that identify and handle spectral artifacts within the context of high-throughput FT-MS-based metabolomics studies. RESULTS: We observed three types of artifacts unique to FT-MS that we named high peak density (HPD) sites: fuzzy sites, ringing and partial ringing. While ringing artifacts are well-known, fuzzy sites and partial ringing have not been previously well-characterized in the literature. We developed new computational methods based on comparisons of peak density within a spectrum to identify regions of spectra with fuzzy sites. We used these methods to identify and eliminate fuzzy site artifacts in an example dataset of paired cancer and non-cancer lung tissue samples and evaluated the impact of these artifacts on classification accuracy and robustness. CONCLUSION: Our methods robustly identified consistent fuzzy site artifacts in our FT-MS metabolomics spectral data. Without artifact identification and removal, 91.4% classification accuracy was achieved on an example lung cancer dataset; however, these classifiers rely heavily on artifactual features present in fuzzy sites. Proper removal of fuzzy site artifacts produces a more robust classifier based on non-artifactual features, with slightly improved accuracy of 92.4% in our example analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-018-1426-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6153687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-61536872018-10-04 New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis Mitchell, Joshua M. Flight, Robert M. Wang, Qing Jun Higashi, Richard M. Fan, Teresa W.-M. Lane, Andrew N. Moseley, Hunter N. B. Metabolomics Original Article INTRODUCTION: Direct injection Fourier-transform mass spectrometry (FT-MS) allows for the high-throughput and high-resolution detection of thousands of metabolite-associated isotopologues. However, spectral artifacts can generate large numbers of spectral features (peaks) that do not correspond to known compounds. Misassignment of these artifactual features creates interpretive errors and limits our ability to discern the role of representative features within living systems. OBJECTIVES: Our goal is to develop rigorous methods that identify and handle spectral artifacts within the context of high-throughput FT-MS-based metabolomics studies. RESULTS: We observed three types of artifacts unique to FT-MS that we named high peak density (HPD) sites: fuzzy sites, ringing and partial ringing. While ringing artifacts are well-known, fuzzy sites and partial ringing have not been previously well-characterized in the literature. We developed new computational methods based on comparisons of peak density within a spectrum to identify regions of spectra with fuzzy sites. We used these methods to identify and eliminate fuzzy site artifacts in an example dataset of paired cancer and non-cancer lung tissue samples and evaluated the impact of these artifacts on classification accuracy and robustness. CONCLUSION: Our methods robustly identified consistent fuzzy site artifacts in our FT-MS metabolomics spectral data. Without artifact identification and removal, 91.4% classification accuracy was achieved on an example lung cancer dataset; however, these classifiers rely heavily on artifactual features present in fuzzy sites. Proper removal of fuzzy site artifacts produces a more robust classifier based on non-artifactual features, with slightly improved accuracy of 92.4% in our example analysis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11306-018-1426-9) contains supplementary material, which is available to authorized users. Springer US 2018-09-17 2018 /pmc/articles/PMC6153687/ /pubmed/30830442 http://dx.doi.org/10.1007/s11306-018-1426-9 Text en © The Author(s) 2018 Open AccessThis 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. |
spellingShingle | Original Article Mitchell, Joshua M. Flight, Robert M. Wang, Qing Jun Higashi, Richard M. Fan, Teresa W.-M. Lane, Andrew N. Moseley, Hunter N. B. New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis |
title | New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis |
title_full | New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis |
title_fullStr | New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis |
title_full_unstemmed | New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis |
title_short | New methods to identify high peak density artifacts in Fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis |
title_sort | new methods to identify high peak density artifacts in fourier transform mass spectra and to mitigate their effects on high-throughput metabolomic data analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6153687/ https://www.ncbi.nlm.nih.gov/pubmed/30830442 http://dx.doi.org/10.1007/s11306-018-1426-9 |
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