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

<p>Abstract</p> <p>Background</p> <p>Nuclear 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.</p> <p>Results</p> <p>We 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.</p> <p>Conclusions</p> <p>The 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 <url>http://code.google.com/p/speaq/</url>.</p

Individual differences in timing of peak positive subjective responses to d-amphetamine: Relationship to pharmacokinetics and physiology

Rate of delivery of psychostimulants has been associated with their positive euphoric effects and potential addiction liability. However, information on individual differences in onset of d-amphetamine's effects remains scarce. We examined individual differences in the time to peak subjective and physiological effects and the pharmacokinetics/pharmacodynamics of oral d-amphetamine. We considered two independent studies that used different dosing regimens where subjects completed the Drug Effects Questionnaire (DEQ) at multiple time points post d-amphetamine. Based on the observation of distinct individual differences in time course of DEQ Feel, High, and Like ratings (DEQ(H+L+F)) in Study 1, subjects in both studies were categorized as Early Peak Responders (peak within 60 minutes), Late Peak Responders (peak > 60 minutes) or Nonresponders; 20-25% of participants were categorized as Early Peak Responders, 50-55% as Late Peak Responders and 20-30% as Nonresponders. Physiological (both studies) and plasma d-amphetamine (Study 1) were compared among these groups. Early Peak Responders exhibited an earlier rise in plasma d-amphetamine levels and more sustained elevation in heart rate compared to Late Peak Responders. The present data illustrate the presence of significant individual differences in the temporal pattern of responses to oral d-amphetamine, which may contribute to heightened abuse potential