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

Date publication

octobre 2020

Journal

Journal of applied statistics

Auteurs

Membres identifiés du Cancéropôle Est :
Pr BOUKHOBZA Taha , Dr DUMOND Hélène

Tous les auteurs :
Bastien B, Boukhobza T, Dumond H, Gégout-Petit A, Muller-Gueudin A, Thiébaut C

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/35706767

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. A simulation 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.

Mots clés

Aggregated methods, correlated covariates selection, genetic profiles, high dimension, multiple testing procedures, personalized medicine, ranking, variable selection

Référence

J Appl Stat. 2020 10 27;49(3):764-781