Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations
Through joint analysis of dynamic (X) and fasting (Y) metabolomics data, our goal is to capture dynamic and static biomarkers of a phenotype for the same subject stratifications providing a complete picture, that may be more effective for precision health - compared to separate analysis of such data sets.
In our recent study, we jointly analyze fasting and dynamic metabolomics data (measured from the plasma samples collected during a meal challenge test) from the COPSAC2000 cohort. For jointly analyzing these data sets, we use coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode.
We demonstrate that such a data fusion approach can extract shared subject stratifications from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Patterns extracted using a data fusion approach in the subjects mode show higher correlations with the phenotype of interest compared to individual analyses of fasting and postprandial data.
See the paper for details:
L. Li, S. Yan, D. Horner, M. A. Rasmussen, A. K. Smilde. E Acar. Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations
With a series of papers [1, 2, 3], we have now discussed the advantages and limitations of various data analysis techniques (CP-based tensor factorizations, coupled matrix and tensor factorizations) in terms of the analysis of time-resolved metabolomics data collected during a meal challenge test. Lu Li will give a talk on this topic at the upcoming Tracer Workshop and also at the SIAM Conference on Applied Linear Algebra.
2. S. Yan, L. Li, D. Horner, P. Ebrahimi, B. Chawes, L. O. Dragsted, M. A. Rasmussen, A. K. Smilde, E. Acar. Characterizing human postprandial metabolic response using multiway data analysis
3. L. Li, S. Yan, B. M. Bakker, H. Hoefsloot, B. Chawes, D. Horner, M. A. Rasmussen, A. K. Smilde, E. Acar. Analyzing postprandial metabolomics data using multiway models: A simulation study, BMC Bioinformatics, 2024
