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Zacharias, H.U. ; Altenbuchinger, M.* ; Gronwald, W.*

Statistical analysis of NMR metabolic fingerprints: Established methods and recent advances.

Metabolites 8:47 (2018)
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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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Publication type Article: Journal article
Document type Review
Keywords Data Normalization ; Data Scaling ; Zero-sum ; Metabolic Fingerprinting ; Nmr ; Statistical Data Analysis; Acute Kidney Injury; Data Sets; Normalization Methods; Variable Selection; Cardiac-surgery; Discrimination; Regression; Urine; Metabonomics; Discovery
Language english
Publication Year 2018
HGF-reported in Year 2018
ISSN (print) / ISBN 2218-1989
e-ISSN 2218-1989
Journal Metabolites
Quellenangaben Volume: 8, Issue: 3, Pages: , Article Number: 47 Supplement: ,
Publisher MDPI
Publishing Place St Alban-anlage 66, Ch-4052 Basel, Switzerland
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
90000 - German Center for Diabetes Research
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-554100-001
G-501900-382
DOI 10.3390/metabo8030047
WOS ID WOS:000445152200008
Scopus ID 85053024502
PubMed ID 30154338
Erfassungsdatum 2018-09-19
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