Analyse de données multi-blocs avec `{RGCCA}`
Le groupe Biopuces et le réseau StateOftheR ont organisé le mardi 9 décembre 2025 de 9h30 à 12h30 un tutoriel donné par Arthur Tenenhaus, Laboratoire des Signaux et Systèmes, CentraleSupelec, Université Paris-Saclay
Title: Multiblock data analysis with {RGCCA} package
Abstract:
Multiblock component methods are essential tools for exploring complex relationships between multiple sets of variables. Although various statistical methods and dedicated software for multiblock data analysis exist, Regularized Generalized Canonical Correlation Analysis (RGCCA) offers a unified and versatile framework that consolidates more than six decades of multiblock component methods. During this half-day practical session, we will present the RGCCA framework and the RGCCA package, which implements this framework and goes beyond by providing graphical outputs and statistical tools to assess the robustness and significance of the analysis. The usefulness of the RGCCA package will be illustrated through two real-world case studies. The package is freely available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=RGCCA and from GitHub at https://github.com/rgcca-factory/RGCCA.
Matériel
Cliquez pour récupérer le support de présentation ainsi que le support pour la partie pratique et les données .
Références
Garali, Imene, Isaac Adanyeguh, Farid Ichou, Vincent Perlbarg, Alexandre Seyer, Benoit Colsch, Ivan Moszer, et al. 2017. “A Strategy for Multimodal Data Integration: Application to Biomarkers Identification in Spinocerebellar Ataxia.” Briefings in Bioinformatics 19 (May): 1356–69. https://doi.org/10.1093/bib/bbx060.
Girka, Fabien, Etienne Camenen, Caroline Peltier, Arnaud Gloaguen, Vincent Guillemot, Laurent Le Brusquet, and Arthur Tenenhaus. 2023. “Multiblock Data Analysis with the RGCCA Package.” Journal of Statistical Software, 1–36. https://CRAN.R-project.org/package=RGCCA.
Girka, Fabien, Arnaud Gloaguen, Laurent Le Brusquet, Violetta Zujovic, and Arthur Tenenhaus. 2024. “Tensor Generalized Canonical Correlation Analysis.” Information Fusion 102 (February). https://doi.org/10.1016/j.inffus.2023.102045.
Gloaguen, Arnaud, Cathy Philippe, Vincent Frouin, Giulia Gennari, Ghislaine Dehaene-Lambertz, Laurent Le Brusquet, and Arthur Tenenhaus. 2022. “Multiway Generalized Canonical Correlation Analysis.” Biostatistics 23 (January): 240–56. https://doi.org/10.1093/biostatistics/kxaa010.
Lucas, Sort, Le Brusquet Laurent, and Tenenhaus Arthur. 2024. “Functional Generalized Canonical Correlation Analysis for Studying Multiple Longitudinal Variables.” Biometrics 00. https://doi.org/10.1093/b.
Tenenhaus, Arthur, Cathy Philippe, and Vincent Frouin. 2015. “Kernel Generalized Canonical Correlation Analysis.” Computational Statistics and Data Analysis 90 (May): 114–31. https://doi.org/10.1016/j.csda.2015.04.004.
Tenenhaus, Arthur, Cathy Philippe, Vincent Guillemot, Kim Anh Le Cao, Jacques Grill, and Vincent Frouin. 2014. “Variable Selection for Generalized Canonical Correlation Analysis.” Biostatistics 15: 569–83. https://doi.org/10.1093/biostatistics/kxu001.
Tenenhaus, Arthur, and Michel Tenenhaus. 2011. “Regularized Generalized Canonical Correlation Analysis.” Psychometrika 76 (April): 257–84. https://doi.org/10.1007/s11336-011-9206-8.
Tenenhaus, Michel, Arthur Tenenhaus, and Patrick J. F. Groenen. 2017. “Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods.” Psychometrika 82 (September): 737–77. https://doi.org/10.1007/s11336-017-9573-x.
