Thao Vu1, Eli Riekeberg2, Yumou Qiu1, Robert Powers3,4. 1. Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, 68583-0963, USA. 2. Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588-0304, USA. 3. Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, 68588-0304, USA. rpowers3@unl.edu. 4. Nebraska Center for Integrated Biomolecular Communication, Lincoln, NE, 68588-0304, USA. rpowers3@unl.edu.
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.
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.
Authors: Aixiang Jiang; Laura K Hilton; Jeffrey Tang; Christopher K Rushton; Bruno M Grande; David W Scott; Ryan D Morin Journal: Blood Cancer Discov Date: 2020-09-10
Authors: Allison Parrett; Joseph M Reed; Stewart G Gardner; Nagendra N Mishra; Arnold S Bayer; Robert Powers; Greg A Somerville Journal: BMC Microbiol Date: 2020-06-15 Impact factor: 3.605