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21 results on '"Rohart, Florian"'

1. Fixed effects Selection in high dimensional Linear Mixed Models

Author

Rohart, Florian, San-Cristobal, Magali, and Laurent, Béatrice

Subjects
Statistics - Computation
Abstract

We consider linear mixed models in which the observations are grouped. A L1-penalization on the fixed effects coefficients of the log-likelihood obtained by considering the random effects as missing values is proposed. A multicycle ECM algorithm is used to solve the optimization problem; it can be combined with any variable selection method developed for linear models. The algorithm allows the number of parameters p to be larger than the total number of observations n; it is faster than the lmmLasso (Schelldorfer,2011) since no n*n matrix has to be inverted. We show that the theoretical results of Schelldorfer (2011) apply for our method when the variances of both the random effects and the residuals are known. The combination of the algorithm with a variable selection method (Rohart 2011) shows good results in estimating the set of relevant fixed effects coefficients as well as estimating the variances; it outperforms the lmmLasso both in the common case (p< n) and in the high-dimensional case (p > n).

Published
2013

2. Multiple Hypotheses Testing For Variable Selection

Author

Rohart, Florian

Subjects
Mathematics - Statistics Theory, Statistics - Methodology
Abstract

Many methods have been developed to estimate the set of relevant variables in a sparse linear model Y= XB+e where the dimension p of B can be much higher than the length n of Y. Here we propose two new methods based on multiple hypotheses testing, either for ordered or non-ordered variables. Our procedures are inspired by the testing procedure proposed by Baraud et al (2003). The new procedures are proved to be powerful under some conditions on the signal and their properties are non asymptotic. They gave better results in estimating the set of relevant variables than both the False Discovery Rate (FDR) and the Lasso, both in the common case (pn).

Published
2011

8. Additional file 1 of MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms

Author

Rohart, Florian, Eslami, Aida, Matigian, Nicholas, StĂŠphanie Bougeard, and Kim-Anh LĂŞ Cao

Abstract

Supplementary material. This pdf document contains supplementary methods and all supplementary Figures and Tables. Specifically, it provides the PLS-algorithm, the extension of MINT in a regression framework, the application to the MAQC data (A vs B), the meta-analysis of the breast cancer data, the classification accuracy of the tested methods on the stem cells and breast cancer data, and details on the signature genes identified by MINT on the stem cells and breast cancer data. (PDF 4403 kb)

Published
2017
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9. Phenotypic diversity spanning the spectrum of hepatocyte differentiation impacts the outcome of patients with beta-catenin-mutated hepatocellular carcinomas

Author

Desert, Romain, Reynes, Christelle, Sabatier, Robert, Gregoire, Damien, Rohart, Florian, Corlu, Anne, Canal, Frederic, Sicard, Marie, Desille, Mireille, Renaud, Stephanie, Turlin, Bruno, Sulpice, Laurent, Bergeat, Damien, Bellaud, Pascale, Perret, Christine, Clement, Bruno, Lê Cao, Kim-Anh, Musso, Orlando, Nutrition, Métabolismes et Cancer (NuMeCan), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Centre de Ressources Biologiques Santé (CRB Santé), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-CHU Pontchaillou [Rennes]-CRLCC Eugène Marquis (CRLCC), Université Paul-Valéry - Montpellier 3 (UPVM), Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Queensland University of Technology [Brisbane] (QUT), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), ProdInra, Archive Ouverte, Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire [Rennes], Université de Rennes (UR), Université de Montpellier (UM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), University of Southern Queensland (USQ), Université Paris Descartes - Paris 5 (UPD5), Centre National de la Recherche Scientifique (CNRS), Nutrition, Métabolismes et Cancer ( NuMeCan ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ), Centre de Ressources Biologiques Santé ( CRB Santé ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -CHU Pontchaillou [Rennes]-CRLCC Eugène Marquis ( CRLCC ), Université Paul-Valéry - Montpellier 3 ( UM3 ), Institut de Génétique Moléculaire de Montpellier ( IGMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Queensland University of Technology [Brisbane] ( QUT ), Institut National de la Santé et de la Recherche Médicale ( INSERM ), Institut Cochin ( UM3 (UMR 8104 / U1016) ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Université de Rennes (UNIV-RENNES), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université de Rennes (UR)-CHU Pontchaillou [Rennes]-CRLCC Eugène Marquis (CRLCC)

Subjects
[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, [ SDV ] Life Sciences [q-bio], [SDV]Life Sciences [q-bio], Médecine humaine et pathologie, Human health and pathology, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017

16. A molecular classification of human mesenchymal stromal cells

Author

Rohart, Florian, primary, Mason, Elizabeth A., additional, Matigian, Nicholas, additional, Mosbergen, Rowland, additional, Korn, Othmar, additional, Chen, Tyrone, additional, Butcher, Suzanne, additional, Patel, Jatin, additional, Atkinson, Kerry, additional, Khosrotehrani, Kiarash, additional, Fisk, Nicholas M., additional, Lê Cao, Kim-Anh, additional, and Wells, Christine A., additional

Published
2016
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17. Exploring transcriptomic diversity in muscle revealed that cellular signaling pathways mainly differentiate five Western porcine breeds

Author

SanCristobal, Magali, primary, Rohart, Florian, additional, Lascor, Christine, additional, Bouffaud, Marcel, additional, Trouilh, Lidwine, additional, Martin, Pascal G.P., additional, Lippi, Yannick, additional, Tribout, Thierry, additional, Faraut, Thomas, additional, Mercat, Marie-José, additional, Milan, Denis, additional, and Liaubet, Laurence, additional

Published
2015
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18. Genetic and phenotypic fine characterizations of French porcine reference populations

Author

San Cristobal, Magali, Boitard, Simon, Fariello Rico, Maria Inès, Gilbert, Hélène, Liaubet, Laurence, Paris, Alain, Gaillard, Claire, Rohart, Florian, Riquet, Juliette, Servin, Bertrand, Villa-Vialaneix, Nathalie, Sanchez, Marie Pierre, Milan, Denis, Laboratoire de Génétique Cellulaire (LGC), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Génétique Animale et Biologie Intégrative (GABI), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), DeLiSus ANR- 07-GANI-001, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences
Abstract

Fine study of the porcine genome. The porcine industry has to face new societal stakes, and propose a more sustainable production. Genetics is one of the control levers. The objective of the DéLiSus project (ANR- 07-GANI-001) was, based on recent developments in genomics, to improve our knowledge on the porcine genome structure, on genetic mechanisms controlling traits, but also connected to the development of new systems of production (such as food efficiency, quality of meat). High throughput genotyping and phenotyping strategy laid on accumulating information on a large number of animals of various porcine breeds, from the gene to the phenotype, and looking for the links between these informations. Thereafter the main results: (1) Large-sized databases with genotypes, transcriptomic, metabolomic and phenotypic data; (2) Contribution to the improvement of the assembly of the porcine genome and to the mapping of 60K SNP; (3) QTL detection (25 phenotypes); (4) Revealing the genetic variability clearly structured; (5) New methodology to detect footprint of selection revealing of genomic regions presenting a strong local decline of variability; (6) Metabolomic profiles allow predicting phenotypes of interest, such as the rate of muscle; and (7)Differential expression between races of numerous genes in the post-mortem muscle (among 44K).

Published
2012

19. Phenotypic prediction based on metabolomic data : lasso vs Bolasso, primary data vs wavelet transformation

Author

Rohart, Florian, Villa-Vialaneix, Nathalie, Paris, Alain, Canlet, Cécile, Molina, J., Milan, Denis, Laurent, Béatrice, San Cristobal, Magali, Institut de Mathématiques de Toulouse UMR5219 (IMT), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Génétique Cellulaire (LGC), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), IUT - Département STID - Carcassonne (UPVD), Université de Perpignan Via Domitia (UPVD), Xénobiotiques, Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université de Toulouse (UT)-Université de Toulouse (UT)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
Abstract

"Chantier qualité spécifique "Auteurs Externes" département de Génétique animale : uniquement liaison auteur au référentiel HR-Access "; International audience; Understanding the relations between various 'omics data (such as metabolomics or genomics data) and phenotypes of interest is one of the current major challenges in biology. This question can be addressed by trying to learn a way to predict the phenotype value from the omic from joint observations of the omic and of the phenotype. In this paper, we focus on the prediction of a phenotype related to the quality of the meat from metabolomic data. As metabolomic data are high dimensional data and as, conjointly, the number of observations is often restricted, model selection methods are a way both to obtain a relevant solution to the prediction problem but also to select the most important metabolomes related to the phenotype under study. During the past years, model selection has know a growing interest in the statistical community: the first - and also probably the most known - selection method has been introducted by \citep{Tibshirani:1996} under the name of LASSO. Several variants of this original approach has then been proposed such as, recently, a bootstraped LASSO, named BOLASSO, introduced in (Bach, 2009). The proposal of this paper is to combine a wavelet representation of the metabolome spectra (see (Mallat, 1999) and (Antonini, 1992) for a complete introduction to wavelets) with the BOLASSO approach. We compare this methodology to more classical methods using either the original spectra as predictors (instead of the wavelet representation) or the original LASSO to select the model. The following section deals with the methodological description of the approach whereas the next one details the experiments and results.

Published
2010

20. A Molecular Classification of Human Mesenchymal Stromal Cells

Author

Rohart, Florian, primary, Mason, Elizabeth, additional, Matigian, Nicholas, additional, Mosbergen, Rowland, additional, Korn, Othmar, additional, Chen, Tyrone, additional, Butcher, Suzanne, additional, Patel, Jatin, additional, Atkinson, Kerry, additional, Khosrotehrani, Kiarash, additional, Fisk, Nicholas, additional, Lê Cao, Kim-Anh, additional, and Wells, Christine A, additional

Published
2015
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