Analyse multi-patients de données génomiques
Document type :
Communication dans un congrès avec actes
Permalink :
Title :
Analyse multi-patients de données génomiques
Author(s) :
Grimonprez, Quentin [Auteur]
Celisse, Alain [Auteur]
Marot, Guillemette [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Celisse, Alain [Auteur]

Marot, Guillemette [Auteur]

MOdel for Data Analysis and Learning [MODAL]
Conference title :
46e Journées de Statistique
Conference organizers(s) :
Société Française de Statistique
City :
Rennes
Country :
France
Start date of the conference :
2014-06-02
Publication date :
2014-06-02
Keyword(s) :
Package
Genomic markers selection
Copy-number
Data normalization
SNP
Segmentation
Genomic markers selection
Copy-number
Data normalization
SNP
Segmentation
HAL domain(s) :
Statistiques [stat]
English abstract : [en]
MPAgenomics, standing for multi-patients analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation, and (ii) genomic marker selection from multi-patient copy number and SNP data profiles.It ...
Show more >MPAgenomics, standing for multi-patients analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation, and (ii) genomic marker selection from multi-patient copy number and SNP data profiles.It provides wrappers from commonly used packages to facilitate their repeated (sometimes difficult) use, offering an easy-to-use pipeline for beginners in R. The segmentation of successive multiple profiles (finding losses and gains) is based on a new automatic choice of influential parameters since default ones were misleading in the original packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given response.Show less >
Show more >MPAgenomics, standing for multi-patients analysis (MPA) of genomic markers, is an R-package devoted to: (i) efficient segmentation, and (ii) genomic marker selection from multi-patient copy number and SNP data profiles.It provides wrappers from commonly used packages to facilitate their repeated (sometimes difficult) use, offering an easy-to-use pipeline for beginners in R. The segmentation of successive multiple profiles (finding losses and gains) is based on a new automatic choice of influential parameters since default ones were misleading in the original packages. Considering multiple profiles in the same time, MPAgenomics wraps efficient penalized regression methods to select relevant markers associated with a given response.Show less >
Language :
Anglais
Audience :
Nationale
Popular science :
Non
Administrative institution(s) :
CHU Lille
CNRS
Université de Lille
CNRS
Université de Lille
Submission date :
2020-06-08T14:10:38Z