Comparing normalization methods and the impact of noise

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...

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Bibliographic Details
Published in:Metabolomics Vol. 14; no. 8; pp. 108 - 10
Main Authors: Vu, Thao, Riekeberg, Eli, Qiu, Yumou, Powers, Robert
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2018
Springer Nature B.V
Subjects:
Biochemistry
Biomedical and Life Sciences
Biomedicine
Cell Biology
Datasets
Developmental Biology
Life Sciences
Metabolomics
Methods
Molecular Medicine
NMR
Noise
Nuclear magnetic resonance
Original Article
Metabolomics
NMR
Normalization
Noise
Preprocessing chemometrics
ISSN:1573-3882, 1573-3890, 1573-3890
Online Access:Get full text
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Summary: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.
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Authors Contribution
TV and ER performed the experiments; RP and YQ designed the experiments; TV, ER, YQ, and RP analyzed the data and wrote the manuscript.
ISSN:1573-3882
1573-3890
1573-3890
DOI:10.1007/s11306-018-1400-6