Description du projet
Les outils statistiques aident à révéler les relations entre les données «omiques» et les caractères spécifiques
Les scientifiques rassemblent des groupes de données dites «omiques» pour mieux comprendre l’évolution des caractères dans le temps. Par exemple, la génomique est l’étude de l’ensemble du génome ou de tous les gènes d’un organisme, et la transcriptomique est l’étude du transcriptome, c’est-à-dire de l’ensemble des transcriptions d’ARN produites par le génome. Les scientifiques analysent ensuite ces deux ensembles et en mesurent les caractéristiques les plus intéressantes, ainsi que leur évolution au fil du temps, dans le cadre d’études à long terme. Le projet ISULO, financé par l’UE, travaille actuellement sur des méthodes statistiques avancées pour analyser simultanément ces deux types de données et explorer les interrelations entre les jeux de données «omiques» dans l’objectif d’obtenir de nouvelles informations importantes sur l’évolution.
Objectif
In medicine and agronomy, there is a growing interest in identifying biological mechanisms involved in the evolution of traits along time. Nowadays, this challenging objective is achieved through the acquisition of high-dimensional –omic datasets from various biological levels, and with the collection of phenotype measures along time on the same individuals, so-called longitudinal data. A new research focus is emerging with the objective to analyze jointly these two types of data. In this project, we propose to develop innovative statistical methods to simultaneously analyze these types of data and to deal with their respective characteristics. Novel methodologies will be developed by combining statistical concepts from linear mixed model and variable selection in a Bayesian framework, and by incorporating or inferring biological relationships. The first aim will focus on the analysis of one or more longitudinal outcomes with one –omic data. Flexible modeling for approximating time-varying covariate effects combined with variable selection approaches will be proposed. Thus, a better understanding of the relationships along time between the outcomes and the relevant covariates will be reached. The second objective is to investigate the integration of multiple –omic datasets for explaining one univariate outcome, then one longitudinal response variable, and finally multivariate longitudinal outcomes. Bayesian hierarchical modeling with prior distributions allowing to capture relationships among –omic datasets will be investigated and new relationships among –omic datasets will be explored. The developments and findings of this project research will greatly contribute to the statistical and biological domains. New generic statistical methods will be developed and will be available for transversal applications in various fields. Finally, this project will highlight the added value brought by a collaborative and interdisciplinary work with experienced researchers.
Champ scientifique
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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
75016 Paris
France