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Normalization techniques for PARAFAC modeling of urine metabolomic data

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Abstract

Introduction

One of the body fluids often used in metabolomics studies is urine. The concentrations of metabolites in urine are affected by hydration status of an individual, resulting in dilution differences. This requires therefore normalization of the data to correct for such differences. Two normalization techniques are commonly applied to urine samples prior to their further statistical analysis. First, AUC normalization aims to normalize a group of signals with peaks by standardizing the area under the curve (AUC) within a sample to the median, mean or any other proper representation of the amount of dilution. The second approach uses specific end-product metabolites such as creatinine and all intensities within a sample are expressed relative to the creatinine intensity.

Objectives

Another way of looking at urine metabolomics data is by realizing that the ratios between peak intensities are the information-carrying features. This opens up possibilities to use another class of data analysis techniques designed to deal with such ratios: compositional data analysis. The aim of this paper is to develop PARAFAC modeling of three-way urine metabolomics data in the context of compositional data analysis and compare this with standard normalization techniques.

Methods

In the compositional data analysis approach, special coordinate systems are defined to deal with the ratio problem. In essence, it comes down to using other distance measures than the Euclidian Distance that is used in the conventional analysis of metabolomic data.

Results

We illustrate using this type of approach in combination with three-way methods (i.e. PARAFAC) of a longitudinal urine metabolomics study and two simulations. In both cases, the advantage of the compositional approach is established in terms of improved interpretability of the scores and loadings of the PARAFAC model.

Conclusion

For urine metabolomics studies, we advocate the use of compositional data analysis approaches. They are easy to use, well established and proof to give reliable results.

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Conflicts of interest

The authors confirm that they have no conflicts of interest.

Funding

This study was funded by the grant 15-34613L of the Czech Science Foundation (GA CR), the projects CZ.1.07/2.3.00/20.0170 and LO1304 of the Ministry of Education, Youth and Sports of the Czech Republic, grant LF_2016_014 by IGA MZČR NT12218, IGUP Olomouc and grant IGA_PrF_2016_025 of the Internal Grant Agency of the Palacký University in Olomouc. The authors gratefully acknowledge to MUDr. Lumír Kantor, Ph.D from Neonatal Department, University Hospital Olomouc, Olomouc, Czech Republic.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Authors and Affiliations

  1. Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, Olomouc, Czech Republic

    Alžběta Gardlo & Karel Hron

  2. Laboratory of Metabolomics, Institute of Molecular and Translational Medicine, University Hospital Olomouc, Palacký University Olomouc, Olomouc, Czech Republic

    Alžběta Gardlo, Marcela Hrdá, Radana Karlíková, David Friedecký & Tomáš Adam

  3. Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands

    Age K. Smilde

  4. Department of Clinical Biochemistry, University Hospital Olomouc, Olomouc, Czech Republic

    David Friedecký & Tomáš Adam

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  1. Alžběta Gardlo
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Correspondence to Alžběta Gardlo.

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Gardlo, A., Smilde, A.K., Hron, K. et al. Normalization techniques for PARAFAC modeling of urine metabolomic data. Metabolomics 12, 117 (2016). https://doi.org/10.1007/s11306-016-1059-9

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