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

Written By Evrim Acar Ataman

Our preprint focusing on the use of tensor factorizations to analyze dynamic metabolomics data is now available. We demonstrate that despite the increasing complexity of the dynamic metabolic models we have studied (i.e., a linear open system, the yeast glycolysis model, the human cholesterol model), tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle various sources of variation and reveal the underlying mechanisms and their dynamics.

For more, see the paper and the Github repo:

L. Li, H. Hoefsloot, A. A. de Graaf, E. Acar, A. K. Smilde. Exploring dynamic metabolomics data with multiway data analysis: A simulation study, 2021.

Lu recently presented this work at the SIAM Conference on Applications of Dynamical Systems on May 23, 2021



Evrim Acar Ataman
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