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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-07-01
|
| Series: | Frontiers in Molecular Biosciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2022.917911/full |
| _version_ | 1818190412963119104 |
|---|---|
| author | Francisco Traquete João Luz Carlos Cordeiro Marta Sousa Silva António E. N. Ferreira |
| author_facet | Francisco Traquete João Luz Carlos Cordeiro Marta Sousa Silva António E. N. Ferreira |
| author_sort | Francisco Traquete |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T23:58:19Z |
| format | Article |
| id | doaj.art-1396d1826fad439a908a492e13e77d40 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-11T23:58:19Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-1396d1826fad439a908a492e13e77d402022-12-22T00:45:18ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-07-01910.3389/fmolb.2022.917911917911Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted MetabolomicsFrancisco TraqueteJoão LuzCarlos CordeiroMarta Sousa SilvaAntónio E. N. FerreiraUntargeted 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.https://www.frontiersin.org/articles/10.3389/fmolb.2022.917911/fulluntargeted metabolomicsmetabolomics data analysismass-difference networksFourier transform mass spectrometrygraph properties |
| spellingShingle | Francisco Traquete João Luz Carlos Cordeiro Marta Sousa Silva António E. N. Ferreira Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics Frontiers in Molecular Biosciences untargeted metabolomics metabolomics data analysis mass-difference networks Fourier transform mass spectrometry graph properties |
| 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 | untargeted metabolomics metabolomics data analysis mass-difference networks Fourier transform mass spectrometry graph properties |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2022.917911/full |
| work_keys_str_mv | AT franciscotraquete graphpropertiesofmassdifferencenetworksforprofilinganddiscriminationinuntargetedmetabolomics AT joaoluz graphpropertiesofmassdifferencenetworksforprofilinganddiscriminationinuntargetedmetabolomics AT carloscordeiro graphpropertiesofmassdifferencenetworksforprofilinganddiscriminationinuntargetedmetabolomics AT martasousasilva graphpropertiesofmassdifferencenetworksforprofilinganddiscriminationinuntargetedmetabolomics AT antonioenferreira graphpropertiesofmassdifferencenetworksforprofilinganddiscriminationinuntargetedmetabolomics |