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Stat Med. 2018 Oct 15;37(23):3338-3356. doi: 10.1002/sim.7821. Epub 2018 Jun 11.

Sparse partial least squares with group and subgroup structure.

Author information

1
ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Australia.
2
Inria, SISTM, Talence and Inserm, U1219 Bordeaux University, Bordeaux, France.
3
Vaccine Research Institute, Creteil, France.
4
Université de Pau et des Pays de l'Adour, Laboratoire de Mathematiques et de leurs Applications, UMR CNRS 5142, Pau, France.

Abstract

Integrative analysis of high dimensional omics datasets has been studied by many authors in recent years. By incorporating prior known relationships among the variables, these analyses have been successful in elucidating the relationships between different sets of omics data. In this article, our goal is to identify important relationships between genomic expression and cytokine data from a human immunodeficiency virus vaccine trial. We proposed a flexible partial least squares technique, which incorporates group and subgroup structure in the modelling process. Our new method accounts for both grouping of genetic markers (eg, gene sets) and temporal effects. The method generalises existing sparse modelling techniques in the partial least squares methodology and establishes theoretical connections to variable selection methods for supervised and unsupervised problems. Simulation studies are performed to investigate the performance of our methods over alternative sparse approaches. Our R package sgspls is available at https://github.com/matt-sutton/sgspls.

KEYWORDS:

feature selection; group variable selection; latent variable modelling; partial least squares

PMID:
29888397
DOI:
10.1002/sim.7821

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