A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles associated with outcome of a non-small-cell lung cancer treatment

Abstract : We propose a new methodology to select and rank covariates associated to a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology imbricates successively 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. The objective of our method is to determine profiles of patients linked with the outcome of a treatment. We apply our method on transcriptomic data of n = 37 patients with advanced non-small-cell lung cancer, who have received chemotherapy. The survival time of these patients being known, we apply our method to select the covariates that are the most linked with the outcome treatment among a set of more than 50 000 transcriptomic covariates. We obtain different transcriptomic profiles for the patients whose survival time was short, versus the other patients with longer survival time.
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https://hal.archives-ouvertes.fr/hal-01939694
Contributor : Aurélie Muller-Gueudin <>
Submitted on : Thursday, November 29, 2018 - 4:36:36 PM
Last modification on : Sunday, April 21, 2019 - 1:17:57 AM

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Bérangère Bastien, Hafid Chakir, Anne Gégout-Petit, Aurélie Muller-Gueudin, Yaojie Shi. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles associated with outcome of a non-small-cell lung cancer treatment. 2018. ⟨hal-01939694⟩

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