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

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.

Automated summary

This article proposes 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 includes clustering, decorrelation, selection, and ranking, and has been validated through simulation studies. Using this method, the researchers successfully selected transcriptomic covariates that explained the survival outcome of chemotherapy in patients with advanced non-small-cell lung cancer, and defined patient profiles for a new metastatic biomarker and associated gene network in breast tumor samples to personalize treatments.