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Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that se...

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Autores principales: Traquete, Francisco, Luz, João, Cordeiro, Carlos, Sousa Silva, Marta, Ferreira, António E. N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353772/
https://www.ncbi.nlm.nih.gov/pubmed/35936789
http://dx.doi.org/10.3389/fmolb.2022.917911
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author Traquete, Francisco
Luz, João
Cordeiro, Carlos
Sousa Silva, Marta
Ferreira, António E. N.
author_facet Traquete, Francisco
Luz, João
Cordeiro, Carlos
Sousa Silva, Marta
Ferreira, António E. N.
author_sort Traquete, Francisco
collection PubMed
description Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.
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spelling pubmed-93537722022-08-06 Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics Traquete, Francisco Luz, João Cordeiro, Carlos Sousa Silva, Marta Ferreira, António E. N. Front Mol Biosci Molecular Biosciences Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9353772/ /pubmed/35936789 http://dx.doi.org/10.3389/fmolb.2022.917911 Text en Copyright © 2022 Traquete, Luz, Cordeiro, Sousa Silva and Ferreira. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Traquete, Francisco
Luz, João
Cordeiro, Carlos
Sousa Silva, Marta
Ferreira, António E. N.
Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics
title Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics
title_full Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics
title_fullStr Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics
title_full_unstemmed Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics
title_short Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics
title_sort graph properties of mass-difference networks for profiling and discrimination in untargeted metabolomics
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353772/
https://www.ncbi.nlm.nih.gov/pubmed/35936789
http://dx.doi.org/10.3389/fmolb.2022.917911
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