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.
https://www.hal.inserm.fr/inserm-01145569
Contributor : Nathalie Duchange
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Submitted on : Friday, April 24, 2015 - 4:01:44 PM
Last modification on : Tuesday, April 2, 2019 - 2:01:12 AM
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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. pp.P28-29 Section imagerie génétique. ⟨inserm-01145569⟩
