Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics |
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Authors: | Paul E Anderson Nicholas V Reo Nicholas J DelRaso Travis E Doom Michael L Raymer |
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Institution: | (1) Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA;(2) Department of Biochemistry and Molecular Biology, Boonshoft School of Medicine, Cox Institute, Wright State University, Dayton, OH 45429, USA;(3) 711 Human Performance Wing, Wright-Patterson AFB, USAF, Dayton, OH 45433, USA |
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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. |
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Keywords: | Gaussian Binning Pattern recognition Quantification Nuclear magnetic resonance |
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