Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models A Rau, C Maugis-Rabusseau, ML Martin-Magniette, G Celeux
Bioinformatics 31 (9), 1420-1427, 2015
65 2015 Transformation and model choice for RNA-seq co-expression analysis A Rau, C Maugis-Rabusseau
Briefings in bioinformatics 19 (3), 425-436, 2018
43 2018 Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis G Rigaill, S Balzergue, V Brunaud, E Blondet, A Rau, O Rogier, J Caius, ...
Briefings in bioinformatics 19 (1), 65-76, 2018
32 2018 Comparing model selection and regularization approaches to variable selection in model-based clustering G Celeux, ML Martin-Magniette, C Maugis-Rabusseau, AE Raftery
Journal de la Societe francaise de statistique (2009) 155 (2), 57, 2014
27 2014 Variable selection in model-based clustering and discriminant analysis with a regularization approach G Celeux, C Maugis-Rabusseau, M Sedki
Advances in Data Analysis and Classification 13 (1), 259-278, 2019
25 2019 Adaptive density estimation for clustering with Gaussian mixtures C Maugis-Rabusseau, B Michel
ESAIM: Probability and Statistics 17, 698-724, 2013
24 2013 On the estimation of mixtures of Poisson regression models with large number of components P Papastamoulis, ML Martin-Magniette, C Maugis-Rabusseau
Computational Statistics & Data Analysis 93, 97-106, 2016
23 2016 Clustering high-throughput sequencing data with Poisson mixture models A Rau, G Celeux, ML Martin-Magniette, C Maugis-Rabusseau
Inria, 2011
22 2011 A sparse variable selection procedure in model-based clustering C Meynet, C Maugis-Rabusseau
19 2012 Clustering transformed compositional data using K -means, with applications in gene expression and bicycle sharing system data A Godichon-Baggioni, C Maugis-Rabusseau, A Rau
Journal of Applied Statistics 46 (1), 47-65, 2019
16 2019 Non-asymptotic detection of two-component mixtures with unknown means B Laurent, C Marteau, C Maugis-Rabusseau
Bernoulli 22 (1), 242-274, 2016
10 2016 Parameter recovery in two-component contamination mixtures: The S Gadat, J Kahn, C Marteau, C Maugis-Rabusseau
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 56 (2 …, 2020
6 2020 SelvarClustMV: Variable selection approach in model-based clustering allowing for missing values C Maugis-Rabusseau, ML Martin-Magniette, S Pelletier
Journal de la Société Française de Statistique 153 (2), 21-36, 2012
6 2012 SelvarMix: Regularization for variable selection in model-based clustering and discriminant analysis M Sedki, G Celeux, C Maugis-Rabusseau
R package version 1 (1), 2017
4 2017 Multidimensional two-component Gaussian mixtures detection B Laurent, C Marteau, C Maugis-Rabusseau
Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 54 (2 …, 2018
2 2018 SelvarMix: AR package for variable selection in model-based clustering and discriminant analysis with a regularization approach M Sedki, G Celeux, C Maugis-Rabusseau
INRIA Techical report, 2014
2 2014 Co-expression analysis of RNA-seq data with the HTSCluster package A Rau, C Maugy-Rabusseau, ML Martin-Magniette, G Celeux
Version 2 (8), 0
1 Insights on the control of yeast single-cell growth variability by members of the Trehalose Phosphate Synthase (TPS) complex S Arabaciyan, M Saint-Antoine, C Maugis-Rabusseau, JM François, ...
Frontiers in cell and developmental biology 9, 2021
2021 Multiview cluster aggregation and splitting, with an application to multiomic breast cancer data A Godichon-Baggioni, C Maugis-Rabusseau, A Rau
Annals of Applied Statistics 14 (2), 752-767, 2020
2020 SuperMix: Sparse Regularization for Mixtures Y de Castro, S Gadat, C Marteau, C Maugis
arXiv preprint arXiv:1907.10592, 2019
2019 