A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology
| Autor: | Hélène Dumond, Charlène Thiébaut, Taha Boukhobza, Bérangère Bastien, Aurélie Muller-Gueudin, Anne Gégout-Petit |
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| Přispěvatelé: | Transgene SA [Illkirch], Centre de Recherche en Automatique de Nancy (CRAN), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Biology, genetics and statistics (BIGS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Élie Cartan de Lorraine (IECL), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Clustering high-dimensional data Variable selection Application Notes Computer science 0211 other engineering and technologies Correlated covariates selection Mathematics - Statistics Theory Context (language use) Feature selection Statistics Theory (math.ST) 02 engineering and technology Machine learning computer.software_genre Statistics - Applications 01 natural sciences Methodology (stat.ME) 010104 statistics & probability [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Covariate FOS: Mathematics Applications (stat.AP) 0101 mathematics Statistics - Methodology [STAT.AP]Statistics [stat]/Applications [stat.AP] 021103 operations research business.industry Genetic profiles High dimension Personalized medicine 3. Good health Variable (computer science) Ranking Multiple testing procedures Aggregated methods Artificial intelligence Statistics Probability and Uncertainty business [STAT.ME]Statistics [stat]/Methodology [stat.ME] computer |
| Zdroj: | Journal of Applied Statistics Journal of Applied Statistics, Taylor & Francis (Routledge), In press, pp.23. ⟨10.1080/02664763.2020.1837083⟩ Journal of Applied Statistics, 2022, 49 (3), pp.764-781. ⟨10.1080/02664763.2020.1837083⟩ J Appl Stat |
| ISSN: | 1360-0532 0266-4763 |
| DOI: | 10.1080/02664763.2020.1837083 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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