Sparse partial least squares with group and subgroup structure
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
THIEBAUT, Rodolphe
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Réduire
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Langue
EN
Article de revue
Ce document a été publié dans
Statistics in Medicine. 2018-10-15, vol. 37, n° 23, p. 3338-3356
Résumé en anglais
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 ...Lire la suite >
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.< Réduire
Mots clés en anglais
SISTM
Unités de recherche