Characterization of 1H NMR Spectroscopic Data and the Generation of Synthetic Validation Sets

Motivation: Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers are forced to rely on indirect performance metrics, which are of limited value. In order to achieve fair and complete analysis of competing techniques more exacting metrics are required. Thus, metabolomics researchers often evaluate their algorithms on simplified simulated data with a known answer. Unfortunately, the conclusions obtained on simulated data are only of value if the data sets are complex enough for results to generalize to true experimental data. Ideally, synthetic data should be indistinguishable from empirical data, yet retain a known best analysis. Results: We have developed a technique for creating realistic synthetic metabolomics validation sets based on NMR spectroscopic data. The validation sets are developed by characterizing the salient distributions in sets of empirical spectroscopic data. Using this technique, several validation sets are constructed with a variety of characteristics present in ‘real’ data. A case study is then presented to compare the relative accuracy of several alignment algorithms using the increased precision afforded by these synthetic data sets. Availability: These data sets are available for download at http://birg.cs.wright.edu/nmr_synthetic_data_sets. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

Localized Deconvolution: Characterizing NMR-Based Metabolomics Spectroscopic Data using Localized High-Throughput Deconvolution

The interpretation of nuclear magnetic resonance (NMR) experimental results for metabolomics studies requires intensive signal processing and multivariate data analysis techniques. Standard quantification techniques attempt to minimize effects from variations in peak positions caused by sample pH, ionic strength, and composition. These techniques fail to account for adjacent signals which can lead to drastic quantification errors. Attempts at full spectrum deconvolution have been limited in adoption and development due to the computational resources required. Herein, we develop a novel localized deconvolution algorithm for general purpose quantification of NMR-based metabolomics studies. Localized deconvolution decreases average absolute quantification error by 97% and average relative quantification error by 88%. When applied to a 1H metabolomics study, the cross-validation metric, Q2, improved 16% by reducing within group variability. This increase in accuracy leads to additional computing costs that are overcome by translating the algorithm to the mapreduce design paradigm

Furosemide Enhances the Sensitivity of Urinary Metabolomics for Assessment of Kidney Function

Introduction The ability of urinary metabolomics to detect meaningful, tissue-specific, biological effects (i.e., toxicity, disease) is compounded by high background variability. We hypothesize that sensitivity can be enhanced by imposing a tissue-targeted metabolic stressor. Objective We tested whether the sensitivity of metabolomics to assess kidney function is improved under the diuretic stress of furosemide. Methods To mildly compromise kidney, rats were given a sub-acute dose of d-serine. Then at 24 h postdose, we administered vehicle solution (control) or the diuretic drug, furosemide, and conducted NMR-based urinary metabolomics. Results Principal Components and OPLS discriminant analyses showed no effects on urinary profiles in rats receiving d-serine alone. However, the effects of d-serine were observable under furosemide-induced stress, as urinary profiles classified separately from rats receiving furosemide alone or vehicle-treated controls (p \u3c 0.001). Furthermore, this profile was uniquely different from a co-treatment effect observed following co-administration of d-serine + furosemide. We identified 24 metabolites to classify the effects of furosemide in normal rats vs. d-serine-compromised rats. Most notably, a furosemide-induced increase in urinary excretion of α-ketoglutarate, creatinine, trigonelline, and tryptophan in control rats, was significantly reduced in d-serine exposed rats (p \u3c 0.05). Interestingly, increased tryptophan metabolism has been shown to correlate with the severity of kidney transplant failure and chronic kidney disease. Conclusions We attribute these effects to differences in kidney function, which were only detectable under the stress imposed by furosemide. This technique may extend to other organ systems and may provide improved sensitivity for assessment of tissue function or early detection of disease

Gaussian Binning: A new Kernel-based Method for processing NMR Spectroscopic Data for Metabolomics

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 dat

Furosemide Enhances the Sensitivity of Urinary Metabolomics for Assessment of Kidney Function

Introduction The ability of urinary metabolomics to detect meaningful, tissue-specific, biological effects (i.e., toxicity, disease) is compounded by high background variability. We hypothesize that sensitivity can be enhanced by imposing a tissue-targeted metabolic stressor. Objective We tested whether the sensitivity of metabolomics to assess kidney function is improved under the diuretic stress of furosemide. Methods To mildly compromise kidney, rats were given a sub-acute dose of d-serine. Then at 24 h postdose, we administered vehicle solution (control) or the diuretic drug, furosemide, and conducted NMR-based urinary metabolomics. Results Principal Components and OPLS discriminant analyses showed no effects on urinary profiles in rats receiving d-serine alone. However, the effects of d-serine were observable under furosemide-induced stress, as urinary profiles classified separately from rats receiving furosemide alone or vehicle-treated controls (p \u3c 0.001). Furthermore, this profile was uniquely different from a co-treatment effect observed following co-administration of d-serine + furosemide. We identified 24 metabolites to classify the effects of furosemide in normal rats vs. d-serine-compromised rats. Most notably, a furosemide-induced increase in urinary excretion of α-ketoglutarate, creatinine, trigonelline, and tryptophan in control rats, was significantly reduced in d-serine exposed rats (p \u3c 0.05). Interestingly, increased tryptophan metabolism has been shown to correlate with the severity of kidney transplant failure and chronic kidney disease. Conclusions We attribute these effects to differences in kidney function, which were only detectable under the stress imposed by furosemide. This technique may extend to other organ systems and may provide improved sensitivity for assessment of tissue function or early detection of disease

Gaussian Binning: A new Kernel-based Method for processing NMR Spectroscopic Data for Metabolomics

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 dat