Analysis of time course 1H NMR metabolomics data by multivariate curve resolution

Modeling NMR‐based metabolomics data often involves linear methods such as principal component analysis (PCA) and partial least squares (PLS). These methods have the objective of describing the main variance in the data and maximum covariance between the predictor variables and some response variabl...

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Bibliographic Details
Published in:Magnetic resonance in chemistry 2009-12, Vol.47 (S1), p.S105-S117
Main Authors: Karakach, Tobias K., Knight, Richard, Lenz, Eva M., Viant, Mark R., Walter, John A.
Format: Article
Language:English
Subjects:
Algorithms
Animals
Biological
embryogenesis
Embryonic Development
Error analysis
Fish
Fishes
Fluctuation
Least squares method
Magnetic Resonance Spectroscopy
Mathematical models
Metabolites
Metabolomics
Multivariate Analysis
multivariate curve resolution
Nephrosis - urine
nephrotoxicity
Oryzias latipes
Rats
Temporal logic
time course metabolomics
Time Factors
Urine - chemistry
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Summary:Modeling NMR‐based metabolomics data often involves linear methods such as principal component analysis (PCA) and partial least squares (PLS). These methods have the objective of describing the main variance in the data and maximum covariance between the predictor variables and some response variable respectively. If the experiment is designed to investigate temporal biological fluctuations, however, the factors obtained become difficult to interpret in a biological context. Moreover, when these methods are applied to analyze data, an implicit assumption is made that the measurement errors exhibit an iid‐normal distribution, often limiting the extent of the information recovered. A method for the linear decomposition of NMR‐based metabolomics data by multivariate curve resolution (MCR), which has been used elsewhere for time course transcriptomics applications, is introduced and implemented via a weighted alternating least squares (ALS) approach. Measurement of error information is incorporated in the modeling process, allowing the least squares projections to be performed in a maximum likelihood fashion. As a result, noise heteroscedasticity resulting from pH‐induced peak shifts can be modeled, eliminating the need for binning/bucketing. The utility of the method is demonstrated using two sets of temporal NMR metabolomics data, HgCl2‐induced nephrotoxicity in rat, and fish (Japanese medaka, Oryzias latipes) embryogenesis. Profiles extracted for the nephrotoxicity data exhibit strong correlations with metabolites consistent with temporal fluctuations in glucosuria. The concentration of metabolites such as acetate, glucose, and alanine exhibit a steady increase, which peaks at Day 3 post dose and returns to basal levels at Day 8. Other metabolites including citrate and 2‐oxoglutarate exhibit the opposite characteristics. Although the fish embryogenesis data are more complex, the profiles extracted by the algorithm display characteristics that depict temporal variation consistent with processes associated with embryogenesis. Copyright © 2009 Crown in the right of Canada. Published by John Wiley & Sons, Ltd Multivariate curve resolution via weighted alternation squares (MCR‐wALS) has been shown to be an effective tool for modeling time course metabolomics data. Two data sets were used to demonstrate the utility of this algorithm. Concentration profiles that qualitatively describe the evolution of metabolites during the course of the experiment were obtained a
ISSN:0749-1581
1097-458X
1097-458X
DOI:10.1002/mrc.2535