Identifying metabotypes from tensor data
Conference poster, 2022
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.
Metabolomics
PARAFAC
Tensor decomposition
Dynamic Mode Decomposition
Author
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
Valencia, Spain,
Subject Categories
Other Computer and Information Science
Bioinformatics and Systems Biology
Nutrition and Dietetics
Areas of Advance
Life Science Engineering (2010-2018)