Dynamic adaptive binning: an improved quantification technique for NMR spectroscopic data

Abstract

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. A key step in this process is the quantification of spectral features, which is commonly accomplished by dividing an NMR spectrum into several hundred integral regions or bins. Binning attempts to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition, while reducing the dimensionality for multivariate statistical analyses. Herein we develop an improved novel spectral quantification technique, dynamic adaptive binning. With this technique, bin boundaries are determined by optimizing an objective function using a dynamic programming strategy. The objective function measures the quality of a bin configuration based on the number of peaks per bin. This technique shows a significant improvement over both traditional uniform binning and other adaptive binning techniques. This improvement is quantified via synthetic validation sets by analyzing an algorithm's ability to create bins that do not contain more than a single peak and that maximize the distance from peak to bin boundary. The validation sets are developed by characterizing the salient distributions in experimental NMR spectroscopic data. Further, dynamic adaptive binning is applied to a 1 H NMRbased experiment to monitor rat urinary metabolites to empirically demonstrate improved spectral quantification.

Multivariate Analysis in Metabolomics

“…Unfortunately, uniform binning incurs the risk of splitting peaks or spectral features between bins, recreating the imprecision in the X variables that the preprocessing set out to correct. "Intelligent" or "adaptive" binning endeavors to evade this problem by using variable bin sizes that avoid dividing peaks between multiple bins [56][57][58][59]. A recent kernelbased method of binning seeks to optimally reduce variable count while retaining spectral information by applying a Gaussian weighting function [57].…”

“…A recent kernelbased method of binning seeks to optimally reduce variable count while retaining spectral information by applying a Gaussian weighting function [57]. Other adaptive binning methods rely on a recursive algorithm [56], undecimated wavelet transforms [58] or the optimization of an objective function using a dynamic programming strategy [59] to identify bin edges. Regardless of the approach, adaptive binning performs significantly better than uniform binning [59].…”

“…Other adaptive binning methods rely on a recursive algorithm [56], undecimated wavelet transforms [58] or the optimization of an objective function using a dynamic programming strategy [59] to identify bin edges. Regardless of the approach, adaptive binning performs significantly better than uniform binning [59]. Alternatively, full-resolution spectral signals may be computationally aligned within a dataset to remove chemical shift variability, retaining the possibility of avoiding binning and performing multivariate analysis with less loss of spectral information [60][61][62][63][64][65].…”

126 Altered Gut Microbial Energy and Metabolism in Children With Non Alcoholic Fatty Liver Disease

“…Quantification of NMR metabolites Quantification of specific metabolite resonances was accomplished using an interactive spectral deconvolution algorithm in MATLAB adapted from our previously described methods (Anderson et al, 2009(Anderson et al, , 2011. The deconvolution tool fits a defined spectral region using a combination of tunable baseline shapes (spline, v-shaped, linear or constant) and a Gauss-Lorentz peakfitting function.…”

Multiple markers for the non-invasive diagnosis and characterisation of prostate cancer

“…However, even in correctly calibrated spectra, individual peaks can still exhibit differences in chemical shift between individual spectra due to differences in sample pH and ionic strength. These can be corrected post-processing by various automatic peak alignment procedures [128][129][130][131][132][133] .…”