  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.
