Gaussian binning: a new kernel-based method for processing NMR spectroscopic data for metabolomics
By Paul E. Anderson, Nicholas V. Reo, Nicholas J. DelRaso, Travis E. Doom, Michael L. Raymer. Metabolomics, 2008
80 citations recorded by the source corpus.
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 J2 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.
Subfields: Analytical Chemistry, Biomedical Engineering, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/5d8bed52e6c68ea74f0751ed940d77fb09b6124b
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Citations in the corpus
- Analytical Methods in Untargeted Metabolomics: State of the Art in 2015 (2015, unknown): “…to mitigate problems such as peaks lying in two consecutive integration regions, some methods have implemented uneven binning algorithms like dynamic adaptative binning (Anderson et al., 2011), Gaussian binning (Anderson et al., 2008), and adaptative intelligent binning (De Meyer et al., 2008).”
- Multivariate Analysis in Metabolomics. (2012, background): ““Intelligent” or “adaptive” binning endeavors to evade this problem by using variable bin sizes that avoid dividing peaks between multiple bins [56-59].”