Integrative analysis of time course metabolic data and biomarker discovery

Jendoubi, Takoua; Ebbels, Timothy M. D.

doi:10.1186/s12859-019-3333-0

Cited by 13 publications

(10 citation statements)

References 51 publications

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“…While we focus on only the analysis of dynamic metabolomics data in this paper, future work includes joint analysis of multiple omics data sets [ 45 ] through extensions of tensor factorizations to coupled matrix and tensor factorizations [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Exploring dynamic metabolomics data with multiway data analysis: a simulation study

Hoefsloot

Graaf

et al. 2022

BMC Bioinformatics

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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.

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Section: Discussionmentioning

confidence: 99%

Exploring dynamic metabolomics data with multiway data analysis: a simulation study

Hoefsloot

Graaf

et al. 2022

BMC Bioinformatics

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“…While we focus on only the analysis of dynamic metabolomics data in this paper, future work includes joint analysis of multiple omics data sets [42] through extensions of tensor factorizations to coupled matrix and tensor factorizations [43].…”

Section: Discussionmentioning

confidence: 99%

Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study

Li¹,

Hoefsloot²,

Graaf³

et al. 2021

Preprint

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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.

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“…Biological entities are complex by nature and are arguably regulated by sequences of actions and complex interactions. In this sense, modeling a sequence of observations naturally regulated by chemical processes has proven successful in computational biology [ 12 ].…”

Section: Study Focusmentioning

confidence: 99%

“…Integrating different omics profiles helps extract insightful information and appreciate more comprehensive snapshots of biological systems and molecular processes. Integrative analysis has been applied to associate omics entities to a phenotype of interest e.g., cardiovascular disease [ 10 ], cancer [ 11 ], or a given treatment or intervention [ 12 ]. Other applications of multi-omics analysis include cross-omics biomarker discovery [ 13 , 14 , 15 ], patient stratification [ 16 , 17 ], and functional analysis [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Jendoubi

2021

Metabolites

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Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria — hypothesis, data types, strategies, study design and study focus — to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

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Integrative analysis of time course metabolic data and biomarker discovery

Cited by 13 publications

References 51 publications

Exploring dynamic metabolomics data with multiway data analysis: a simulation study

Exploring dynamic metabolomics data with multiway data analysis: a simulation study

Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

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