A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Applied Statistics Année : 2022

A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology

Résumé

We propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. Simulations study shows the interest of the decorrelation inside the different clusters of covariates. We first apply our method to transcriptomic data of 37 patients with advanced non-small-cell lung cancer who have received chemotherapy, to select the transcriptomic covariates that explain the survival outcome of the treatment. Secondly, we apply our method to 79 breast tumor samples to define patient profiles for a new metastatic biomarker and associated gene network in order to personalize the treatments.
Fichier principal
Vignette du fichier
MainArticle_HAL_Gueudin.pdf (2.14 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-02173568 , version 1 (11-09-2019)

Identifiants

Citer

Bérangère Bastien, Taha Boukhobza, Hélène Dumond, Anne Gégout-Petit, Aurélie Muller-Gueudin, et al.. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology. Journal of Applied Statistics, 2022, 49 (3), pp.764-781. ⟨10.1080/02664763.2020.1837083⟩. ⟨hal-02173568⟩
236 Consultations
172 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More