Abstract : Sparse generalized canonical correlation analysis (SGCCA) has been proposed to combine RGCCA with an ℓ 1-penalty in a unified framework. Within this framework, blocks are not necessarily fully connected, which provides flexibility. The versatility and usefulness of SGCCA are illustrated on a 3-block dataset which combine Gene Expression, Comparative Genomic Hybridiza-tion and tumor location, determined on RMI at diagnosis. All data were measured on a cohort of 53 children with High Grade Glioma. SGCCA is available on CRAN as part of the RGCCA package.
http://www.hal.inserm.fr/inserm-01145569
Contributeur : Nathalie Duchange
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Soumis le : vendredi 24 avril 2015 - 16:01:44
Dernière modification le : vendredi 31 août 2018 - 09:03:57
Document(s) archivé(s) le : mercredi 19 avril 2017 - 05:57:10
Cathy Philippe, Arthur Tenenhaus, Vincent Guillemot, Jacques Grill, Vincent Frouin. Multiblock analysis of omics and imaging data with variable selection. Journées RITS 2015, Mar 2015, Dourdan, France. Actes des Journées RITS 2015, pp.P28-29 Section imagerie génétique, 2015. 〈inserm-01145569〉
