Combining strong sparsity and competitive predictive power with the L-sOPLS approach for biomarker discovery in metabolomics

Abstract

of PLS which promotes an inner variable/feature selection, is an interesting existing solution. But a new intuitive algorithm is proposed in this paper to combine sparsity and the advantages of an orthogonalization step: the "Light-sparse-OPLS" (L-sOPLS). L-sOPLS promotes sparsity on a previously optimized deflated matrix which implies the removal of the Y-orthogonal components. Results A discussion around the compromise between sparsity and predictive modelling performances is provided and it is shown that L-sOPLS produces convincing results, illustrated principally on the basis of 1 H-NMR spectral data but also on genomic RT-qPCR data. Conclusion The L-sOPLS algorithm allows to reach better predictive performances than (O)PLS and sPLS while taking into account only a very small number of relevant descriptors.

Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size

“…The gain in prediction performance is often not worth the complexity of the final model. Furthermore, interesting interpretation properties could also be reached via orthogonal projection to latent structures (OPLS) method which removes variation from the predictors matrix that is not correlated to the outcome (Trygg and Wold, 2002;Féraud et al, 2017). In particular, OPLS modeling of a univariate outcome requires only one predictive component.…”

A review on the application of chromatographic methods, coupled to chemometrics, for food authentication

“…O-PLS is another multivariate calibration method with as main objective improving the interpretation of PLS models and reduce model complexity. O-PLS provides a way to remove variation from an input data set X not correlated to the response y; in other words it removes variability in X that is orthogonal to y (Féraud et al, 2017). This can be performed by subtracting PLS components, orthogonal to y, from the X data.…”