Metabolomics datasets generated by modern analytical instruments tend to be increasingly complex. In this study, a recent method named shrunken centroids regularized discriminant analysis (SCRDA) has been introduced and applied in the exploration of metabolomics dataset. It is a supervised method for variable selection, discriminant analysis and biomarker screening. By regularizing the estimate of the within‐class covariance matrix, SCRDA can deal with the singularity issue of linear discriminant analysis. Then a shrinkage estimator is applied to perform variable selection. The method presented is illustrated through the simulated datasets and three complex metabolomics datasets. Commonly used orthogonal partial least squares discriminant analysis and two other similar statistical methods, penalized linear discriminant analysis and nearest shrunken centroids, are used for comparisons. The results illustrate that SCRDA has some desirable abilities in variable selection, classification and prediction. Moreover, the biomarkers identified by SCRDA are further demonstrated to be in accordance with the biochemical research. It has been proved that SCRDA can be applied as a promising strategy in metabolomics. Copyright © 2014 John Wiley & Sons, Ltd.

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(1) Background: Alternating interhemispheric slow-wave activity during sleep is well-established in birds and cetaceans, but its investigation in humans has been largely neglected. (2) Methods: Fuzzy entropy was used to calculate a laterality index (LI) from C3 and C4 EEG channels. The subjects were grouped according to an apnoea-hypopnoea index (AHI) for statistical analyses: Group A AHI < 15 (mild); Group B 15 ≤ AHI < 30 (moderate); Group C AHI ≥ 30 (severe). The LI distribution was analysed to characterise the brain activity variation in both hemispheres, and the cross-zero switching rate was given statistical tests to find the correlations with the severity of obstructive sleep apnea and sleep states, i.e., wake (W), light sleep (LS), deep sleep (DS), and REM. (3) Results: EEG brain switching activity was observed in all sleep stages, and the LI distribution shows that, for obstructive sleep apnea patients, the interhemispheric asymmetry of brain activity is more obvious than healthy people. A one-way ANOVA revealed a significant difference of switching rate among three groups (F(2,95) = 7.23, p = 0.0012), with Group C shows the least, and also a significant difference among four sleep stages (F(3,94) = 5.09, p = 0.0026), with REM the highest. (4) Conclusions: The alternating interhemispheric activity is confirmed ubiquitous for humans during sleep, and sleep-disordered breathing intends to exacerbate the interhemispheric asymmetry.