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Metabolites 2018, 8(3), 47; https://doi.org/10.3390/metabo8030047

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

1
Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
2
Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Am Biopark 9, 93053 Regensburg, Germany
3
Institute of Functional Genomics, University of Regensburg, Am Biopark 9, 93053 Regensburg, Germany
*
Author to whom correspondence should be addressed.
Received: 2 July 2018 / Revised: 1 August 2018 / Accepted: 18 August 2018 / Published: 28 August 2018
(This article belongs to the Section Thematic Reviews)
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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. View Full-Text
Keywords: data normalization; data scaling; zero-sum; metabolic fingerprinting; NMR; statistical data analysis data normalization; data scaling; zero-sum; metabolic fingerprinting; NMR; statistical data analysis
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Zacharias, H.U.; Altenbuchinger, M.; Gronwald, W. Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances. Metabolites 2018, 8, 47.

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