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Gomtsyan M, Lévy-Leduc C, Ouadah S, Sansonnet L, Blein T. Variable selection in sparse GLARMA models. STATISTICS-ABINGDON 2022. [DOI: 10.1080/02331888.2022.2090943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
| | | | - Sarah Ouadah
- Université Paris-Saclay, AgroParisTech, Paris, France
| | | | - Thomas Blein
- Institute of Plant Sciences Paris-Saclay, Université Evry, Université Paris-Saclay, Université de Paris, Orsay, France
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Grandclaudon M, Perrot-Dockès M, Trichot C, Karpf L, Abouzid O, Chauvin C, Sirven P, Abou-Jaoudé W, Berger F, Hupé P, Thieffry D, Sansonnet L, Chiquet J, Lévy-Leduc C, Soumelis V. A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication. Cell 2020; 179:432-447.e21. [PMID: 31585082 DOI: 10.1016/j.cell.2019.09.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 06/20/2019] [Accepted: 09/09/2019] [Indexed: 12/24/2022]
Abstract
Cell-cell communication involves a large number of molecular signals that function as words of a complex language whose grammar remains mostly unknown. Here, we describe an integrative approach involving (1) protein-level measurement of multiple communication signals coupled to output responses in receiving cells and (2) mathematical modeling to uncover input-output relationships and interactions between signals. Using human dendritic cell (DC)-T helper (Th) cell communication as a model, we measured 36 DC-derived signals and 17 Th cytokines broadly covering Th diversity in 428 observations. We developed a data-driven, computationally validated model capturing 56 already described and 290 potentially novel mechanisms of Th cell specification. By predicting context-dependent behaviors, we demonstrate a new function for IL-12p70 as an inducer of Th17 in an IL-1 signaling context. This work provides a unique resource to decipher the complex combinatorial rules governing DC-Th cell communication and guide their manipulation for vaccine design and immunotherapies.
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Affiliation(s)
- Maximilien Grandclaudon
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; INSERM U932, Immunity and Cancer, 75005 Paris, France
| | - Marie Perrot-Dockès
- UMR MIA-Paris, AgroParisTech, INRA-Université Paris-Saclay, 75005 Paris, France
| | - Coline Trichot
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; INSERM U932, Immunity and Cancer, 75005 Paris, France
| | - Léa Karpf
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; INSERM U932, Immunity and Cancer, 75005 Paris, France
| | - Omar Abouzid
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; INSERM U932, Immunity and Cancer, 75005 Paris, France
| | - Camille Chauvin
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; INSERM U932, Immunity and Cancer, 75005 Paris, France
| | - Philémon Sirven
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; INSERM U932, Immunity and Cancer, 75005 Paris, France
| | - Wassim Abou-Jaoudé
- Computational Systems Biology Team, Institut de Biologie de l'École Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, INSERM U1024, École Normale Supérieure, PSL Université, 75005 Paris, France
| | - Frédérique Berger
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; Institut Curie, PSL Research University, Unit of Biostatistics, 75005 Paris, France; Institut Curie, PSL Research University, INSERM U900, 75005 Paris, France
| | - Philippe Hupé
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; Institut Curie, PSL Research University, INSERM U900, 75005 Paris, France; Mines Paris Tech, 77305 Cedex Fontainebleau, France
| | - Denis Thieffry
- Computational Systems Biology Team, Institut de Biologie de l'École Normale Supérieure, Centre National de la Recherche Scientifique UMR8197, INSERM U1024, École Normale Supérieure, PSL Université, 75005 Paris, France
| | - Laure Sansonnet
- UMR MIA-Paris, AgroParisTech, INRA-Université Paris-Saclay, 75005 Paris, France
| | - Julien Chiquet
- UMR MIA-Paris, AgroParisTech, INRA-Université Paris-Saclay, 75005 Paris, France
| | - Céline Lévy-Leduc
- UMR MIA-Paris, AgroParisTech, INRA-Université Paris-Saclay, 75005 Paris, France
| | - Vassili Soumelis
- Institut Curie, Centre de Recherche, PSL Research University, 75005 Paris, France; INSERM U932, Immunity and Cancer, 75005 Paris, France.
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Denis C, Lebarbier E, Lévy‐Leduc C, Martin O, Sansonnet L. A novel regularized approach for functional data clustering: an application to milking kinetics in dairy goats. J R Stat Soc Ser C Appl Stat 2020. [DOI: 10.1111/rssc.12404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- C. Denis
- AgroParisTech Institut National de la Recherche Agronomique Paris France
- Université Paris‐Saclay Paris France
- Université Paris‐Est Champs‐sur‐Marne France
| | - E. Lebarbier
- AgroParisTech Institut National de la Recherche Agronomique Paris France
- Université Paris‐Saclay Paris France
| | - C. Lévy‐Leduc
- AgroParisTech Institut National de la Recherche Agronomique Paris France
- Université Paris‐Saclay Paris France
| | - O. Martin
- AgroParisTech Institut National de la Recherche Agronomique Paris France
- Université Paris‐Saclay Paris France
| | - L. Sansonnet
- AgroParisTech Institut National de la Recherche Agronomique Paris France
- Université Paris‐Saclay Paris France
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Perrot-Dockès M, Lévy-Leduc C, Chiquet J, Sansonnet L, Brégère M, Étienne MP, Robin S, Genta-Jouve G. A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data. Stat Appl Genet Mol Biol 2018; 17:/j/sagmb.ahead-of-print/sagmb-2017-0077/sagmb-2017-0077.xml. [PMID: 30205662 DOI: 10.1515/sagmb-2017-0077] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. Applying statistical procedures that do not adjust the variable selection step to the dependence pattern may result in a loss of power and the selection of spurious variables. The goal of this paper is to propose a variable selection procedure within the multivariate linear model framework that accounts for the dependence between the multiple responses. We shall focus on a specific type of dependence which consists in assuming that the responses of a given individual can be modelled as a time series. We propose a novel Lasso-based approach within the framework of the multivariate linear model taking into account the dependence structure by using different types of stationary processes covariance structures for the random error matrix. Our numerical experiments show that including the estimation of the covariance matrix of the random error matrix in the Lasso criterion dramatically improves the variable selection performance. Our approach is successfully applied to an untargeted LC-MS (Liquid Chromatography-Mass Spectrometry) data set made of African copals samples. Our methodology is implemented in the R package MultiVarSel which is available from the Comprehensive R Archive Network (CRAN).
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Affiliation(s)
- Marie Perrot-Dockès
- UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France
| | - Céline Lévy-Leduc
- UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France
| | - Julien Chiquet
- UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France
| | - Laure Sansonnet
- UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France
| | - Margaux Brégère
- UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France
| | - Marie-Pierre Étienne
- UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France
| | - Stéphane Robin
- UMR MIA-Paris, AgroParisTech, INRA - Université Paris-Saclay, 75005 Paris, France
| | - Grégory Genta-Jouve
- UMR CNRS 8638 Comète - Université Paris-Descartes, CNRS, 75006 Paris, France
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