Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances

Metabolites
Helena U ZachariasWolfram Gronwald

Abstract

In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification of differential metabolites and the classification of specimens. However, analysis results strongly depend on the preprocessing of the data, and there is no consensus yet on how to remove unwanted biases and experimental variance prior to statistical analysis. Here, we first review established and new preprocessing protocols and illustrate their pros and cons, including different data normalizations and transformations. Second, we give a brief overview of state-of-the-art statistical analysis in NMR-based metabolomics. Finally, we discuss a recent development in statistical data analysis, where data normalization becomes obsolete. This method, called zero-sum regression, builds metabolite signatures whose estimation as well as predictions are independent of prior normalization.

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Citations

Nov 18, 2018·Electrophoresis·Biswapriya B Misra, Subhashree Mohapatra
Apr 12, 2020·European Journal of Mass Spectrometry·Biswapriya B Misra
Jun 19, 2019·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Federica De CastroFrancesco Paolo Fanizzi
Mar 20, 2019·Metabolites·Matteo StoccheroGiuseppe Giordano
Aug 7, 2021·Metabolites·Ulla T SchultheissHelena U Zacharias

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Methods Mentioned

BETA
nuclear magnetic resonance
NMR
PCA

Software Mentioned

DA
NormalizeMets
PLS
SIMCA
MetaPre
MetaboAnalyst
MATLAB
MVAPACK
R
OPLS

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