Regularization tools for multivariate analysis: application to multi-omics (2016-2019)
Marie’s PhD was co-supervised with Céline Lévy-Leduc (50%/50%) and Laure Sansonnet, from 2016 to 2019.
Summary
This thesis develops regularized methods for variable selection in high-dimensional multivariate linear models, where the response is a vector of correlated outcomes rather than a single variable. It proposes procedures that jointly estimate the regression coefficients and the covariance structure of the residuals, improving selection accuracy when responses are strongly dependent, and establishes theoretical guarantees for the resulting estimators. The methodology is implemented in the MultiVarSel R package and applied to metabolomics data (LC-MS), where it is used to identify which molecular features are jointly associated with experimental conditions while accounting for the complex correlation structure between metabolites.
PhD manucript
2019
PhD thesis
Méthodes régularisées pour l’analyse de données multivariées en grande dimension: théorie et applications
This thesis develops regularized methods for variable selection in high-dimensional multivariate linear models, where the response is a vector of correlated outcomes rather than a single variable. It proposes procedures that jointly estimate the regression coefficients and the covariance structure of the residuals, improving selection accuracy when responses are strongly dependent, and establishes theoretical guarantees for the resulting estimators. The methodology is implemented in the MultiVarSel R package and applied to metabolomics data (LC-MS), where it is used to identify which molecular features are jointly associated with experimental conditions while accounting for the complex correlation structure between metabolites.
Journal papers
2019
Cell
A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication
Maximilien Grandclaudon, Marie Perrot-Dockès, Coline Trichot, and 9 more authors
@article{mgrandclaudon_tmod,title={A Quantitative Multivariate Model of Human Dendritic
Cell-T Helper Cell Communication},author={Grandclaudon, Maximilien and Perrot-Dockès, Marie and Trichot, Coline and Mostafa-Abouzid, Omar and Abou-Jaoudé, Wassim and Berger, Frédérique and Hupé, Philippe and Thieffry, Denis and Sansonnet, Laure and Chiquet, Julien and Levy-Leduc, Céline and Soumelis, Vassili},journal={Cell},volume={179},number={2},pages={432-447},year={2019},doi={10.1016/j.cell.2019.09.012},}
2018
SAGMB
A multivariate variable selection approach for analyzing LC-MS metabolomics data
M. Perrot, C. Lévy-Leduc, Julien Chiquet, and 5 more authors
@article{2018_jmva_perrot,author={Perrot, M. and Lévy-Leduc, C. and Sansonnet, L. and Chiquet, Julien},title={Variable selection in multivariate linear models
with high-dimensional covariance matrix estimation},journal={J. Multivar. Anal.},fjournal={Journal of Multivariate Analysis},volume={166},pages={78--97},year={2018},publisher={Elsevier},doi={10.1016/j.jmva.2018.02.006},}
Conferences
2018
Multivariate statistical modelling for QTL detection and marker selection in a bi-parental grapevine population
Charlotte Brault, Marie Perrot-Dockès, Agnes Doligez, and 3 more authors
In 17. Meeting of the EUCARPIA Section Biometrics in Plant Breeding, 2018
2018
Sélection de variables dans le modèle linéaire multivarié en grande dimension avec prise en compte de la dépendance
M. Perrot, C. Lévy-Leduc, J. Chiquet, and 1 more author
In actes des 50\ieme journées françaises de statistique, Saclay, 2018
2017
Modèle linéaire multivarié parcimonieux avec estimation de covariance : une application à des données de métabolomique
M. Perrot-Dockes, C. Lévy-Leduc, J. Chiquet, and 1 more author
In actes des 49\ieme journées françaises de statistique, Avignon, 2017