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

Understand

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.