Hierarchical approach for integrating various genomic data sets

Denis Marie, Tadesse Mahlet. 2015. Hierarchical approach for integrating various genomic data sets. . ASA. Seattle : ASA, Résumé, 1 p. JSM 2015 (Joint Statistical Meetings), Seattle, États-Unis, 8 Août 2015/13 Août 2015.

Matériel d'accompagnement : 1 diaporama (22 vues)

Résumé : Advances in high-throughput technologies have led to the acquisition of various types of -omic data on the same biological samples. Each data type gives independent and complementary information that can explain the biological mechanisms of interest. While several studies performing independent analyses of each dataset have led to significant results, a better understanding of complex biological mechanisms requires an integrative analysis of different sources of -omic data. The proposed approach allows the integration of various genomic data types at the gene level by considering biological relationships between the different molecular features. Several scenarios and a flexible modeling, based on penalized likelihood approaches and EM algorithms, are studied and tested. The method is applied to genomic datasets from Glioblastoma Multiforme samples collected as part of the Cancer Genome Atlas project in order to elucidate biological mechanisms of the disease and identify markers associated with patients' survival.

Mots-clés libres : Hierarchical approach, Integration of -omic data, Penalized likelihood, EM algorithm

Classification Agris : U10 - Informatique, mathématiques et statistiques
000 - Autres thèmes
L10 - Génétique et amélioration des animaux
L73 - Maladies des animaux
U30 - Méthodes de recherche

Auteurs et affiliations

• Denis Marie, CIRAD-BIOS-UMR AGAP (FRA)
• Tadesse Mahlet, CIRAD-ES-UPR BSef (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/580170/)

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