@article {43012,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	journal = {bioRxiv},
	year = {2023},
	publisher = {bioRxiv},
	doi = {https://doi.org/10.1101/2022.12.19.521154 },
	url = {https://www.biorxiv.org/content/10.1101/2022.12.19.521154v2},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Horner, David and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42752,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {Norwegian Bioinformatics Days, Sundvolden, Norway},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42657,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {NuGOweek 2022 in Spain},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42691,
	title = {Analyzing postprandial metabolomics data using multiway models: A simulation study},
	howpublished = {Nordic Metabolomics 2022, Copenhagen, Denmark},
	year = {2022},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Horner, David and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@article {28151,
	title = {Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study},
	journal = {BMC Bioinformatics},
	volume = {23},
	number = {Article 31},
	year = {2022},
	month = {2022},
	publisher = {Springer },
	abstract = {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. In this paper, we study the use of multiway data analysis to reveal the underlying patterns and dynamics in time-resolved metabolomics data.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.},
	doi = {https://doi.org/10.1186/s12859-021-04550-5},
	author = {Li, Lu and Hoefsloot, Huub and Albert A. {de Graaf} and Evrim Acar {Ataman} and Age K. {Smilde}}
}
@misc {42656,
	title = {Revealing dynamic changes in metabolism through the analysis of postprandial metabolomics data: A simulation study},
	year = {2022},
	address = {Metabolomics 2022, Valencia, Spain},
	author = {Li, Lu and Yan, Shi and Barbara M. {Bakker} and Hoefsloot, Huub and Chawes, Bo and Morten A. {Rasmussen} and Age K. {Smilde} and Evrim Acar {Ataman}}
}
@misc {42486,
	title = {Exploring dynamic metabolomics data with multiway data analysis: A simulation study},
	howpublished = {Virtual conference},
	year = {2021},
	publisher = {SIAM Conference on Applications of Dynamical Systems},
	abstract = {Analysis of dynamic metabolomics data sets holds the promise to improve our understanding of the underlying mechanisms in human metabolism. That is crucial to detect the changes in the metabolism that can potentially lead to diseases. Dynamic metabolomics data has more than two axes of variation, i.e., samples, metabolites and time. While such time-evolving multi-way data sets are collected more and more in recent years, revealing the underlying mechanisms and their dynamics from such data remains challenging.\&nbsp;\&nbsp;This talk will focus on a systematic study demonstrating the advantages and limitations of multi-way data analysis (also known as tensor factorizations) in terms of analyzing dynamic metabolomics data. We study different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, a human cholesterol model, and generate data with different types of variation. Our numerical experiments demonstrate that despite the increasing complexity of the studied models, tensor factorization methods CANDECOMP/PARAFAC(CP) and PARAllel\&nbsp;Profiles\&nbsp;with\&nbsp;LINear\&nbsp;Dependences\&nbsp;(PARALIND) can reveal the underlying mechanisms and their dynamics.},
	author = {Li, Lu and Hoefsloot, Huub and Albert A. {de Graaf} and Evrim Acar {Ataman} and Age K. {Smilde}}
}
