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NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data

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Abstract

Introduction

In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.

Objectives

We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers.

Methods

We present NormalizeMets—a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/. The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets.

Results

NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis.

Conclusion

NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user’s own computer allowing for reproducible code to be stored locally.

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Acknowledgements

Professor Terry Speed, Walter and Eliza Hall Institute of Medical Research.

Funding

Julie A. Simpson is supported by a National Health and Medical Research Council (NHMRC) Senior Research Fellowship (1104975). Alysha M. De Livera is supported by The University of Melbourne Research Fellowship. Darren J. Creek is supported by a National Health and Medical Research Council (NHMRC) Career Development Research Fellowship (1088855).

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Author notes

  1. Alysha M. De Livera, Gavriel Olshansky, Julie A. Simpson and Darren J. Creek have contributed equally to this study.

Authors and Affiliations

  1. Biostatistics Unit, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3800, Australia

    Alysha M. De Livera & Julie A. Simpson

  2. Department of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, 3800, Australia

    Gavriel Olshansky

  3. Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, 3052, Australia

    Darren J. Creek

Authors
  1. Alysha M. De Livera
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  2. Gavriel Olshansky
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  3. Julie A. Simpson
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  4. Darren J. Creek
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Corresponding author

Correspondence to Alysha M. De Livera.

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De Livera, A.M., Olshansky, G., Simpson, J.A. et al. NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data. Metabolomics 14, 54 (2018). https://doi.org/10.1007/s11306-018-1347-7

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