Hochrein, Jochen and Zacharias, Helena U. and Taruttis, Franziska and Samol, Claudia and Engelmann, Julia C. and Spang, Rainer and Oefner, Peter J. and Gronwald, Wolfram (2015) Data Normalization of H-1 NMR Metabolite Fingerprinting Data Sets in the Presence of Unbalanced Metabolite Regulation. JOURNAL OF PROTEOME RESEARCH, 14 (8). pp. 3217-3228. ISSN 1535-3893, 1535-3907
Full text not available from this repository. (Request a copy)Abstract
Data normalization is an essential step in NMR-based metabolomics. Conducted properly, it improves data quality and removes unwanted biases. The choice of the appropriate normalization method is critical and depends on the inherent properties of the data set in question. In particular, the presence of unbalanced metabolic regulation, where the different specimens and cohorts under investigation do not contain approximately equal shares of up- and down-regulated features, may strongly influence data normalization. Here, we demonstrate the suitability of the Shapiro-Wilk test to detect such unbalanced regulation. Next, employing a Latin-square design consisting of eight metabolites spiked into a urine specimen at eight different known concentrations, we show that commonly used normalization and scaling methods fail to retrieve true metabolite concentrations in the presence of increasing amounts of glucose added to simulate unbalanced regulation. However, by learning the normalization parameters on a subset of nonregulated features only, Linear Baseline Normalization, Probabilistic Quotient Normalization, and Variance Stabilization Normalization were found to account well for different dilutions of the samples without distorting the true spike-in levels even in the presence of marked unbalanced metabolic regulation. Finally, the methods described were applied successfully to a real world example of unbalanced regulation, namely, a set of plasma specimens collected from patients with and without acute kidney injury after cardiac surgery with cardiopulmonary bypass use.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | METABOLOMICS DATA; OLIGONUCLEOTIDE ARRAYS; URINE; SPECTROSCOPY; METABONOMICS; DISEASE; NMR; data normalization; metabolomics; unbalanced regulation; confounding factors |
| 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: | Dr. Gernot Deinzer |
| Date Deposited: | 25 Jun 2019 13:27 |
| Last Modified: | 25 Jun 2019 13:27 |
| URI: | https://pred.uni-regensburg.de/id/eprint/5114 |
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