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Exploring dynamic metabolomics data with multiway data analysis: a simulation study
BACKGROUND: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array wit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750750/ https://www.ncbi.nlm.nih.gov/pubmed/35012453 http://dx.doi.org/10.1186/s12859-021-04550-5 |
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author | Li, Lu Hoefsloot, Huub de Graaf, Albert A. Acar, Evrim Smilde, Age K. |
author_facet | Li, Lu Hoefsloot, Huub de Graaf, Albert A. Acar, Evrim Smilde, Age K. |
author_sort | Li, Lu |
collection | PubMed |
description | BACKGROUND: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. RESULTS: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. CONCLUSION: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04550-5. |
format | Online Article Text |
id | pubmed-8750750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87507502022-01-11 Exploring dynamic metabolomics data with multiway data analysis: a simulation study Li, Lu Hoefsloot, Huub de Graaf, Albert A. Acar, Evrim Smilde, Age K. BMC Bioinformatics Research BACKGROUND: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. To explore the performance of multiway data analysis methods in terms of revealing the underlying mechanisms in dynamic metabolomics data, simulated data with known ground truth can be studied. RESULTS: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth. CONCLUSION: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04550-5. BioMed Central 2022-01-10 /pmc/articles/PMC8750750/ /pubmed/35012453 http://dx.doi.org/10.1186/s12859-021-04550-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Li, Lu Hoefsloot, Huub de Graaf, Albert A. Acar, Evrim Smilde, Age K. Exploring dynamic metabolomics data with multiway data analysis: a simulation study |
title | Exploring dynamic metabolomics data with multiway data analysis: a simulation study |
title_full | Exploring dynamic metabolomics data with multiway data analysis: a simulation study |
title_fullStr | Exploring dynamic metabolomics data with multiway data analysis: a simulation study |
title_full_unstemmed | Exploring dynamic metabolomics data with multiway data analysis: a simulation study |
title_short | Exploring dynamic metabolomics data with multiway data analysis: a simulation study |
title_sort | exploring dynamic metabolomics data with multiway data analysis: a simulation study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750750/ https://www.ncbi.nlm.nih.gov/pubmed/35012453 http://dx.doi.org/10.1186/s12859-021-04550-5 |
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