Zacharias, Helena U. and Altenbuchinger, Michael and Gronwald, Wolfram (2018) Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances. METABOLITES, 8 (3): 47. ISSN 2218-1989
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | ACUTE KIDNEY INJURY; DATA SETS; NORMALIZATION METHODS; VARIABLE SELECTION; CARDIAC-SURGERY; DISCRIMINATION; REGRESSION; URINE; METABONOMICS; DISCOVERY; data normalization; data scaling; zero-sum; metabolic fingerprinting; NMR; statistical data analysis |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner) Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) |
| Depositing User: | Petra Gürster |
| Date Deposited: | 10 Jul 2020 10:37 |
| Last Modified: | 10 Jul 2020 10:37 |
| URI: | https://pred.uni-regensburg.de/id/eprint/13949 |
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