Shrunken centroids regularized discriminant analysis as a promising strategy for metabolomics data exploration

Abstract

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.

Application of multivariate statistical techniques in microbial ecology

“…OPLS-DA, SVM, and RF all showed good performance in different comparative studies, with RF currently being favored in microbial community comparisons (Knights et al , 2011). To avoid the problem of model overfitting, model regularization and variable selection and filtering should be applied during discriminatory analyses, especially for datasets with large number of variables (e.g., OTU tables) (Chen et al , 2015). …”

“…While the initial high-throughput studies tended to employ simpler exploratory techniques such as PCA and PCoA, there is a recent trend to move to interpretive and discriminatory ordination approaches and to perform more rigorous multivariate hypothesis testing. The use of more recently developed techniques including principal curves and surfaces (De’ath, 1999; Hastie, Stuetzle, 1989), dynamic factor analysis (Zuur et al , 2003), random-effect ordination (Walker, Jackson, 2011), coreferentiality (Fesel, 2012), multidimensional fuzzy set ordination (Roberts, 2009) and fuzzy clustering (Bezdek, 1981), elastic net regression (Zou, Hastie, 2005), and regularized discriminant analyses (Chen et al , 2015; Friedman, 1989; Zhao, Wong, 2014) is expected to further advance these studies and improve the validity and robustness of the outcomes. All these techniques provide exciting opportunities to link ecological and functional measures of microbial communities with environmental gradients, host (patient) information, and time and space variables.…”

Chemometrics in instrumental analysis of complex systems—in honor and memory of Yi‐Zeng Liang

“…Bin et al proposed supervised components and explored their application in NIR and Raman data modeling . Shrunken centroids regularized discriminant analysis (SCRDA) has been introduced and applied in the exploration of metabolomic datasets, representing a supervised method for variable selection, discriminant analysis, and biomarker screening . Sparse linear discriminant analysis (SLDA) can perform classification and variable selection simultaneously to analyze complicated metabolomic datasets …”

Chemometrics in China

“…In the first decade of the millennium, this number reached a peak of nine Chinese papers among the total 68 research papers in 2009. In the current decade, there are 20 Chinese papers among the total 58 research papers in 2015. With such expanding representation, it seems appropriate to present a special issue highlighting the best of Chinese research.…”

“…We were deeply saddened by the passing of Professor Yizeng Liang during the compilation of this special issue. Professor Liang published 26 research papers in the Journal of Chemometrics . In volume 6, 113 to 116, 1992, a laboratory profile and a photo of the members of Bergen chemometrics group was published featuring Liang.…”