NDLI: Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics

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Metabolomics : Volume 4, Issue 2, June 2008

Metabolomics : Volume 4, Issue 1, March 2008

Metabolomics : Volume 3

Metabolomics : Volume 2

Metabolomics : Volume 1

Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics

Content Provider	Springer Nature Link
Author	Anderson, Paul E. Reo, Nicholas V. DelRaso, Nicholas J. Doom, Travis E. Raymer, Michael L.
Copyright Year	2008
Abstract	In many metabolomics studies, NMR spectra are divided into bins of fixed width. This spectral quantification technique, known as uniform binning, is used to reduce the number of variables for pattern recognition techniques and to mitigate effects from variations in peak positions; however, shifts in peaks near the boundaries can cause dramatic quantitative changes in adjacent bins due to non-overlapping boundaries. Here we describe a new Gaussian binning method that incorporates overlapping bins to minimize these effects. A Gaussian kernel weights the signal contribution relative to distance from bin center, and the overlap between bins is controlled by the kernel standard deviation. Sensitivity to peak shift was assessed for a series of test spectra where the offset frequency was incremented in 0.5 Hz steps. For a 4 Hz shift within a bin width of 24 Hz, the error for uniform binning increased by 150%, while the error for Gaussian binning increased by 50%. Further, using a urinary metabolomics data set (from a toxicity study) and principal component analysis (PCA), we showed that the information content in the quantified features was equivalent for Gaussian and uniform binning methods. The separation between groups in the PCA scores plot, measured by the J $_{2}$ quality metric, is as good or better for Gaussian binning versus uniform binning. The Gaussian method is shown to be robust in regards to peak shift, while still retaining the information needed by classification and multivariate statistical techniques for NMR-metabolomics data.
Starting Page	261
Ending Page	272
Page Count	12
File Format	PDF
ISSN	15733882
Journal	Metabolomics
Volume Number	4
Issue Number	3
e-ISSN	15733890
Language	English
Publisher	Springer US
Publisher Date	2008-08-20
Publisher Institution	Metabolomics Society
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gaussian Binning Pattern recognition Quantification Nuclear magnetic resonance Biomedicine general Developmental Biology Cell Biology Molecular Medicine Biochemistry
Content Type	Text
Resource Type	Article
Subject	Endocrinology, Diabetes and Metabolism Clinical Biochemistry Biochemistry

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