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We propose a new methodology for selecting and ranking covariates associatedwith a variable of interest in a context of high-dimensional data underdependence but few observations. The methodology successively intertwines theclustering of covariates, decorrelation of covariates using Factor LatentAnalysis, selection using aggregation of adapted methods and finally ranking.Simulations study shows the interest of the decorrelation inside the differentclusters of covariates. We first apply our method to transcriptomic data of 37patients with advanced non-small-cell lung cancer who have receivedchemotherapy, to select the transcriptomic covariates that explain the survivaloutcome of the treatment. Secondly, we apply our method to 79 breast tumorsamples to define patient profiles for a new metastatic biomarker andassociated gene network in order to personalize the treatments.

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