Skip to main content

Comparing normalization methods and the impact of noise

Metabolomics volume 14, Article number: 108 (2018) Cite this article

Abstract

Introduction

Failure to properly account for normal systematic variations in OMICS datasets may result in misleading biological conclusions. Accordingly, normalization is a necessary step in the proper preprocessing of OMICS datasets. In this regards, an optimal normalization method will effectively reduce unwanted biases and increase the accuracy of downstream quantitative analyses. But, it is currently unclear which normalization method is best since each algorithm addresses systematic noise in different ways.

Objective

Determine an optimal choice of a normalization method for the preprocessing of metabolomics datasets.

Methods

Nine MVAPACK normalization algorithms were compared with simulated and experimental NMR spectra modified with added Gaussian noise and random dilution factors. Methods were evaluated based on an ability to recover the intensities of the true spectral peaks and the reproducibility of true classifying features from orthogonal projections to latent structures—discriminant analysis model (OPLS-DA).

Results

Most normalization methods (except histogram matching) performed equally well at modest levels of signal variance. Only probabilistic quotient (PQ) and constant sum (CS) maintained the highest level of peak recovery (> 67%) and correlation with true loadings (> 0.6) at maximal noise.

Conclusion

PQ and CS performed the best at recovering peak intensities and reproducing the true classifying features for an OPLS-DA model regardless of spectral noise level. Our findings suggest that performance is largely determined by the level of noise in the dataset, while the effect of dilution factors was negligible. A minimal allowable noise level of 20% was also identified for a valid NMR metabolomics dataset.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Abbreviations

NMR:

Nuclear magnetic resonance

PCA:

Principal components analysis

OPLS-DA:

Orthogonal projections to latent structures—discriminant analysis

PQ:

Probabilistic quotient

HM:

Histogram matching

SNV:

Standard normal variate

MSC:

Multiplicative scatter correction

Q:

Quantile

CSpline:

Natural cubic splines

SSpline:

Smoothing splines

CS:

Constant sum

ROI:

Region of interest

PSC:

Phase-scatter correction

LOESS:

LOcally Estimated Scatterplot Smoothing

ROC:

Receiver operating characteristic curve

1D:

One-dimensional

SD:

Standard deviation

References

Download references

Acknowledgements

We thank Dr. Martha Morton, the Director of the Research Instrumentation Facility in the Department of Chemistry at the University of Nebraska-Lincoln for her assistance with the NMR experiments. This material is based upon work supported by the National Science Foundation under Grant Number (1660921). This work was supported in part by funding from the Redox Biology Center (P30 GM103335, NIGMS); and the Nebraska Center for Integrated Biomolecular Communication (P20 GM113126, NIGMS). The research was performed in facilities renovated with support from the National Institutes of Health (RR015468-01). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Affiliations

  1. Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, 68583-0963, USA

    Thao Vu & Yumou Qiu

  2. Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588-0304, USA

    Eli Riekeberg & Robert Powers

  3. Nebraska Center for Integrated Biomolecular Communication, Lincoln, NE, 68588-0304, USA

    Robert Powers

Authors
  1. Thao Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eli Riekeberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yumou Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert Powers
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

TV and ER performed the experiments; RP and YQ designed the experiments; TV, ER, YQ, and RP analyzed the data and wrote the manuscript.

Corresponding author

Correspondence to Robert Powers.

Ethics declarations

Conflict of interest

Authors have no conflict of interest to declare.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 579 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vu, T., Riekeberg, E., Qiu, Y. et al. Comparing normalization methods and the impact of noise. Metabolomics 14, 108 (2018). https://doi.org/10.1007/s11306-018-1400-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11306-018-1400-6

Keywords

  • Metabolomics
  • Normalization
  • Noise
  • NMR
  • Preprocessing chemometrics