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

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. 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.

Characterizing human postprandial metabolic response using multiway data analysis

“…Extracted patterns are unique under mild conditions (25,37) and this facilitates interpretation. While CP-based tensor methods have been previously used in longitudinal data analysis, e.g., analysis of gut microbiome (38), urine metabolomics (39), and simulated dynamic metabolomics data (40), its application in postprandial metabolomics data analysis has been limited. We recently simulated postprandial metabolomics data using a human whole-body metabolic model (41), and demonstrated that analysis of the T0-corrected data (i.e., the postprandial data corrected by subtracting the fasting state data, similar to the method of analysis of changes ( 42)) using a CP model together with the analysis of the fasting state data provides a comprehensive picture of the underlying metabolic mechanisms.…”

Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches

“…For instance, through the analysis of longitudinal metabolomics data as well as data from other sources, it may be possible to capture early signs of diseases (Price et al, 2017 ). Recently, tensor factorizations have been used to analyze dynamic metabolomics data (Li et al, 2022 ) but how to capture evolving patterns from such data is yet to be studied.…”

Data-driven analysis and prediction of dynamic postprandial metabolic response to multiple dietary challenges using dynamic mode decomposition

“…The potential metabotype clusters identified using pDMDc were compared to clusters derived from the scores of the unconstrained CP ( 30 ), representing a state-of-the-art tensor decomposition method ( Equation 25 ).…”