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Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

BMC Bioinformatics · 2011 · Vol. 12(1) · pp. 253–253

Kim‐Anh Lê Cao✉(University of Queensland)Simon Boitard(Laboratoire de Génétique Cellulaire)Philippe Besse(Centre National de la Recherche Scientifique)

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Abstract

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.

Gene expression and cancer classification Spectroscopy and Chemometric Analyses Molecular Biology Techniques and ApplicationsInterpretabilityFeature selectionLinear discriminant analysisComputer scienceArtificial intelligenceSelection (genetic algorithm)Machine learningData miningDiscriminantPattern recognition (psychology)

MeSH terms

Polymorphism, Single NucleotideArtificial IntelligenceHumansNeoplasmsDiscriminant AnalysisOligonucleotide Array Sequence AnalysisGene Expression Profiling