An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data

Abstract

BackgroundNuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline.ResultsWe introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data.ConclusionsThe workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from http://code.google.com/p/speaq/.

Nuclear magnetic resonance metabolomic profiling of urine provides a noninvasive alternative to the identification of biomarkers associated with endometriosis

“…Regions of d 6.50-4.32 ppm, including the residual water and urea resonances, and the baseline region of d 9.26-9.15 ppm were excluded from the analysis. Then spectra were aligned with the use of the hierarchic cluster-based peak alignment algorithm (''Speaq'' R package) (18) and normalized by means of probabilistic quotient normalization (19). Subsequently, the resulting bucket table was transformed into a data matrix of 653 consecutive rectangular buckets (0.01 ppm) with the use of the ''Chemospec'' R package (20) to facilitate the statistical analysis.…”

Multivariate Analysis in Metabolomics

“…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]. Spectral alignment has been accomplished using a variety of approaches that includes fuzzy warping, genetic algorithms, a generalized fuzzy Hough transform approach, a reduced set mapping (PARS) algorithm, or a recursive segment-wise peak alignment.…”

NMR-based metabolomics: From sample preparation to applications in nutrition research

“…Another method for alignment uses a hierarchical cluster tree to align a target spectrum to the reference spectrum. The cluster tree is built from the peak lists from the reference and target spectra and the spectra are divided into segments and aligned [27]. In addition to the above-mentioned examples, other algorithms have been published to achieve peak alignment [28][29][30].…”