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

By Kim-Anh Lê Cao, Simon Boitard, Philippe C. Besse. BMC Bioinformatics, 2011

892 citations recorded by the source corpus.

BackgroundVariable 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.ResultsA simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework.ConclusionssPLS-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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9b3a3144646ab7888a50a64d180a1a41c2228e44

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Citations in the corpus

Grammar of protein domain architectures (2019, methodology): “To this end, we analyzed bigram language models of major subdivisions of life (bacteria, archaea, green plants, fungi, and animals) by using sparse partial least squares discriminant analysis (sPLS-DA) (77) to identify the bigrams that contributed maximally to the differentiation of each clade from ”
A review of variable selection methods in Partial Least Squares Regression (2012, methodology): “ST-PLS has been used for selection of relevant genes for genotype phenotype mapping [69] and Sparse-PLS has been used for biomarker selection from omics data [70] and for SNPs selection [71], and codes for Sparse”
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis (2018, methodology): “Based on additional user feedback, we have also added two variants of the popular partial least square (PLS) methods, including orthogonal PLS and sparse PLS for improved data interpretation and more robust statistical analysis (44,45).”
mixOmics: An R package for ‘omics feature selection and multiple data integration (2017, methodology): “In single ‘omics analyses this distance achieves best accuracy when the predicted values are close to 0 or 1 [10].”
Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (2014, methodology): “presented a computational methodology called sparse PLS to perform variable selection in a multiclass classification framework [54].”