Identifying metabotypes from tensor data
Conference poster, 2022

Metabolic response to diet shows large individual variation, which warrants tailored dietary recommendation i.e., personalized nutrition (PN). A step towards PN is to tailor diet to groups of individuals with similar metabolic phenotype, so called metabotypes (i.e., clusters of individuals with similar metabolism). Metabotyping of high-dimensional data is commonly performed in matrix form using matrix decompositions (e.g., PCA). However, data from e.g., crossover studies can be conveniently organized in multi-dimensional form (i.e., as tensor data) and methods for detecting metabotypes in such data are still lacking.

We therefore aimed to develop and evaluate tools to identify potential metabotypes in high-dimensional tensor data.

We developed two methods: The first uses CANDECOMP/PARAFAC (CP) decomposition directly on tensor data where clustering was performed on individual’s scores, whereas the second was developed specifically for time-resolved data and uses dynamic mode decomposition (DMD) to model metabolite dynamics, where clustering was performed on individual’s dynamic state trajectories. We applied the methods to identify metabotypes in data from a crossover acute post-prandial dietary intervention study on 17 overweight males (BMI 25–30 kg/m2, 41–67 y of age) undergoing three dietary interventions (pickled herring, baked herring and baked beef, measuring 79 metabolites (from GC-MS metabolomics) at 8 time points (0-7h).

Both methods identified two potential metabotype clusters, predominantly in amino acids after the meat diet. The clustering associated to baseline levels of creatinine, strengthening the plausibility of found metabotypes. The CP method is a general approach, not specific to time-resolved data, and provides better fit if the data is multilinear. Conversely, DMD is designed for time-resolved data, for which it often provides a better fit than CP. We concluded that both the CP and the DMD approach are well suited to identify metabotypes in tensor data from a wide variety of complex experimental designs.



Tensor decomposition

Dynamic Mode Decomposition


Viktor Skantze

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Mikael Wallman

Fraunhofer-Chalmers Centre

Ann-Sofie Sandberg

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Rikard Landberg

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

Mats Jirstrand

Fraunhofer-Chalmers Centre

Carl Brunius

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

18th International Conference of the Metabolomics Society
Valencia, Spain,

Subject Categories

Other Computer and Information Science

Bioinformatics and Systems Biology

Nutrition and Dietetics

Areas of Advance

Life Science Engineering (2010-2018)

More information

Latest update