Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems
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Updated: May 31, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Kim-Anh Lê Cao1, Simon Boitard, Philippe Besse
1Queensland Facility for Advanced Bioinformatics, University of Queensland, 4072 St Lucia, QLD, Australia. k.lecao@uq.edu.au
Background:
Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits.
Results:
A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework.
Conclusions:
sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.
