publications

jss

Stability Selection and Consensus Clustering in R: The R Package sharp

Barbara Bodinier, Sabrina Rodrigues, Maryam Karimi, Sarah Filippi, Julien Chiquet, and Marc Chadeau-Hyam

submitted

Code
bioRxiv

Microbial association networks give relevant insights into plant pathobiomes

Charlie Pauvert, Tania Fort, Agnès Calonnec, Julie Faivre-d’Arcier, Emilie Chancerel, Marie Massot, Julien Chiquet, Stéphane Robin, David A. Bohan, Jessica Vallance, and Corinne Vacher

2020

Abs bioRxiv

Interactions between plant pathogens and other plant-associated microorganisms regulate disease. Deciphering the networks formed by these interactions, termed pathobiomes, is crucial to disease management. Our aim was to investigate whether microbial association networks inferred from metabarcoding data give relevant insights into pathobiomes, by testing whether inferred associations contain signals of ecological interactions. We used Poisson Lognormal Models to construct microbial association networks from metabarcoding data and then investigated whether some of these associations corresponded to interactions measurable in co-cultures or known in the literature, by using grapevine (Vitis vinifera) and the fungal pathogen causing powdery mildew (Erysiphe necator) as a model system. Our model suggested that the pathogen species was associated with 23 other fungal species, forming its putative pathobiome. These associations were not known as interactions in the literature, but one of them was confirmed by our co-culture experiments. The yeast Buckleyzyma aurantiaca impeded pathogen growth and reproduction, in line with the negative association found in the microbial network. Co-cultures also supported another association involving two yeast species. Together, these findings indicate that microbial networks can provide plausible hypotheses of ecological interactions that could be used to develop microbiome-based strategies for crop protection.

2024

Hair-Derived Exposome Exploration of Cardiometabolic Health: Piloting a Bayesian Multitrait Variable Selection Approach

Rin Wada, Feng-Jiao Peng, Chia-An Lin, Roel Vermeulen, Alba Iglesias-González, Paul Palazzi, Barbara Bodinier, Sylvie Streel, Michèle Guillaume, Dragana Vuckovic, and others

Environmental Science & Technology, 2024

2023

Bioinformatics

Automated calibration of consensus weighted distance-based clustering approaches using sharp

Barbara Bodinier, Sabrina Rodrigues, Maryam Karimi, Sarah Filippi, Julien Chiquet, and Marc Chadeau-Hyam

Bioinformatics, 2023

arXiv
Plant Commun.

Cell specialization and coordination in Arabidopsis leaves upon pathogenic attack revealed by scRNA-seq

Etienne Delannoy, Bastien Batardiere, Stéphanie Pateyron, Ludivine Soubigou-Taconnat, Julien Chiquet, Jean Colcombet, and Julien Lang

Plant Communications, 2023

Focus Issue on Plant Single-Cell Biology

Abs bioRxiv

Plant defense responses involve several biological processes that allow plants to fight against pathogenic attacks. How these different processes are orchestrated within organs and depend on specific cell types is poorly known. Here, using single-cell RNA sequencing (scRNA-seq) technology on three independent biological replicates, we identified several cell populations representing the core transcriptional responses of wild-type Arabidopsis leaves inoculated with the bacterial pathogen Pseudomonas syringae DC3000. Among these populations, we retrieved major cell types of the leaves (mesophyll, guard, epidermal, companion, and vascular S cells) with which we could associate characteristic transcriptional reprogramming and regulators, thereby specifying different cell-type responses to the pathogen. Further analyses of transcriptional dynamics, on the basis of inference of cell trajectories, indicated that the different cell types, in addition to their characteristic defense responses, can also share similar modules of gene reprogramming, uncovering a ubiquitous antagonism between immune and susceptible processes. Moreover, it appears that the defense responses of vascular S cells, epidermal cells, and mesophyll cells can evolve along two separate paths, one converging toward an identical cell fate, characterized mostly by lignification and detoxification functions. As this divergence does not correspond to the differentiation between immune and susceptible cells, we speculate that this might reflect the discrimination between cell-autonomous and non-cell-autonomous responses. Altogether our data provide an upgraded framework to describe, explore, and explain the specialization and the coordination of plant cell responses upon pathogenic challenge.
JRSS-C

Automated calibration for stability selection in penalised regression and graphical models

Barbara Bodinier, Sarah Filippi, Therese Haugdahl Nøst, Julien Chiquet, and Marc Chadeau-Hyam

Journal of the Royal Statistical Society Series C: Applied Statistics, 2023

Abs arXiv

Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an in-house stability score and accommodating a priori-known block structure (e.g. multi-OMIC) data. It applies to [Least Absolute Shrinkage Selection Operator (LASSO)] penalised regression and graphical models. Simulations show our approach outperforms non-stability-based and stability selection approaches using the original calibration. Application to multi-block graphical LASSO on real (epigenetic and transcriptomic) data from the Norwegian Women and Cancer study reveals a central/credible and novel cross-OMIC role of LRRN3 in the biological response to smoking. Proposed approaches were implemented in the R package sharp.

2022

Neurips

A Probabilistic Graph Coupling View of Dimension Reduction

Hugues Van Assel, Thibault Espinasse, Julien Chiquet, and Franck Picard

In Advances in Neural Information Processing Systems, 2022

Bib

@inproceedings{assel2022a,
  title = {A Probabilistic Graph Coupling View of Dimension
                    Reduction},
  author = {Assel, Hugues Van and Espinasse, Thibault and Chiquet, Julien and Picard, Franck},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Oh, Alice H. and Agarwal, Alekh and Belgrave, Danielle and Cho, Kyunghyun},
  year = {2022},
  url = {https://openreview.net/forum?id=tNXumks8yHv},
}

Comput. Stat.
Adjusting the adjusted Rand Index - A multinomial story

Martina Sundqvist, Julien Chiquet, and Guillem Rigaill

Computational Statistics, 2022

Abs arXiv Bib Code

The Adjusted Rand Index (ARI) is arguably one of the most popular measures for cluster comparison. The adjustment of the ARI is based on a hypergeometric distribution assumption which is not satisfactory from a modeling point of view because (i) it is not appropriate when the two clusterings are dependent, (ii) it forces the size of the clusters, and (iii) it ignores the randomness of the sampling. In this work, we present a new "modified" version of the Rand Index. First, as in Russell et al. (J Malar Inst India 3(1), 1940 ), we consider only the pairs consistent by similarity and ignore the pairs consistent by difference to define the MRI. Second, we base the adjusted version, called MARI, on a multinomial distribution instead of a hypergeometric distribution. The multinomial model is advantageous because it does not force the size of the clusters, correctly models randomness and is easily extended to the dependent case. We show that ARI is biased under the multinomial model and that the difference between ARI and MARI can be significant for small n but essentially vanishes for large n, where n is the number of individuals. Finally, we provide an efficient algorithm to compute all these quantities ((A)RI and M(A)RI) based on a sparse representation of the contingency table in our aricode package. The space and time complexity is linear with respect to the number of samples and, more importantly, does not depend on the number of clusters as we do not explicitly compute the contingency table.
@article{Sundqvist2022, author = {Sundqvist, Martina and Chiquet, Julien and Rigaill, Guillem}, title = {Adjusting the adjusted Rand Index - A multinomial story}, journal = {Computational Statistics}, doi = {10.1007/s00180-022-01230-7}, year = {2022}, }
J. Stat. Softw.
missSBM: An R Package for Handling Missing Values in the Stochastic Block Model

Pierre Barbillon, Julien Chiquet, and Timothée Tabouy

Journal of Statistical Software, 2022

Abs arXiv Bib Code

<p>The stochastic block model is a popular probabilistic model for random graphs. It is commonly used to cluster network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting a stochastic block model to a partially observed network, it is important to consider the underlying process that generates the missing values, otherwise the inference may be biased. This paper presents missSBM, an R package that fits stochastic block models when the network is partially observed, i.e., the adjacency matrix contains not only 1s or 0s encoding the presence or absence of edges, but also NAs encoding the missing information between pairs of nodes. This package implements a set of algorithms to adjust the binary stochastic block model, possibly in the presence of external covariates, by performing variational inference suitable for several observation processes. Our implementation automatically explores different block numbers to select the most relevant model according to the integrated classification likelihood criterion. The integrated classification likelihood criterion can also help determine which observation process best fits a given dataset. Finally, missSBM can be used to perform imputation of missing entries in the adjacency matrix. We illustrate the package on a network dataset consisting of interactions between political blogs sampled during the 2007 French presidential election.</p>
@article{JSSv101i12, title = {missSBM: An R Package for Handling Missing Values in the Stochastic Block Model}, volume = {101}, url = {https://www.jstatsoft.org/index.php/jss/article/view/v101i12}, doi = {10.18637/jss.v101.i12}, number = {12}, journal = {Journal of Statistical Software}, author = {Barbillon, Pierre and Chiquet, Julien and Tabouy, Timothée}, year = {2022}, pages = {1–32}, }

2021

Adv. Ecol. Res.
Chapter Nine - Coupling ecological network analysis with high-throughput sequencing-based surveys: Lessons from the next-generation biomonitoring project

Maxime Dubart, Pascal Alonso, Didac Barroso-Bergada, Nathalie Becker, Kevin Bethune, David A. Bohan, Christophe Boury, Marine Cambon, Elsa Canard, Emilie Chancerel, Julien Chiquet, Patrice David, Natasha de Manincor, Sophie Donnet, Anne Duputié, Benoît Facon, Erwan Guichoux, Tâm Le Minh, Sebastián Ortiz-Martínez, Lucie Piouceau, Ambre Sacco-Martret de Préville, Manuel Plantegenest, Céline Poux, Virginie Ravigné, Stéphane Robin, Marine Trillat, Corinne Vacher, Christian Vernière, and François Massol

In The Future of Agricultural Landscapes, Part III, 2021

Abs HAL Bib

Biomonitoring ecosystems is necessary in order to evaluate risks and to efficiently manage ecosystems and their associated services. Agrosystems are the target of multiple stressors that can affect many species through effects cascading along food webs. However, classic biomonitoring, focused on species diversity or indicator species, might be a poor predictor of the risk of such whole-ecosystem perturbations. Thanks to high-throughput sequencing methods, however, it might be possible to obtain sufficient information about entire ecological communities to infer the functioning of their associated interaction networks, and thus monitor more closely the risk of the collapse of entire food webs due to external stressors. In the course of the ‘next-generation biomonitoring’ project, we collectively sought to experiment with this idea of inferring ecological networks on the basis of metabarcoding information gathered on different systems. We here give an overview of issues and preliminary results associated with this endeavour and highlight the main difficulties that such next-generation biomonitoring is still facing. Going from sampling protocols up to methods for comparing inferred networks, through biomolecular, bioinformatic, and network inference, we review all steps of the process, with a view towards generality and transferability towards other systems.
@incollection{DUBART2021367, title = {Chapter Nine - Coupling ecological network analysis with high-throughput sequencing-based surveys: Lessons from the next-generation biomonitoring project}, editor = {Bohan, David A. and Dumbrell, Alex J. and Vanbergen, Adam J.}, series = {Advances in Ecological Research}, publisher = {Academic Press}, volume = {65}, pages = {367-430}, year = {2021}, hal = {hal-03634351v1}, booktitle = {The Future of Agricultural Landscapes, Part III}, issn = {0065-2504}, doi = {https://doi.org/10.1016/bs.aecr.2021.10.007}, url = {https://www.sciencedirect.com/science/article/pii/S0065250421000222}, author = {Dubart, Maxime and Alonso, Pascal and Barroso-Bergada, Didac and Becker, Nathalie and Bethune, Kevin and Bohan, David A. and Boury, Christophe and Cambon, Marine and Canard, Elsa and Chancerel, Emilie and Chiquet, Julien and David, Patrice and {de Manincor}, Natasha and Donnet, Sophie and Duputié, Anne and Facon, Benoît and Guichoux, Erwan and {Le Minh}, Tâm and Ortiz-Martínez, Sebastián and Piouceau, Lucie and {Sacco-Martret de Préville}, Ambre and Plantegenest, Manuel and Poux, Céline and Ravigné, Virginie and Robin, Stéphane and Trillat, Marine and Vacher, Corinne and Vernière, Christian and Massol, François}, keywords = {Ecological networks, Network inference, Next-generation biomonitoring, High throughput sequencing, Logic-based machine learning, Next-generation sequencing, Microbiomes, eDNA}, }
G3
Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

Charlotte Brault, Agnès Doligez, Loïc Le Cunff, Aude Coupel-Ledru, Thierry Simonneau, Julien Chiquet, Patrice This, and Timothée Flutre

G3: Genes|Genomes|Genetics, 2021

Abs bioRxiv HAL Bib

Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.
@article{Brault2021, title = {Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine}, author = {Brault, Charlotte and Doligez, Agn{\`e}s and Le Cunff, Lo{\"i}c and Coupel-Ledru, Aude and Simonneau, Thierry and Chiquet, Julien and This, Patrice and Flutre, Timoth{\'e}e}, year = {2021}, doi = {10.1093/g3journal/jkab248}, biorxiv = {10.1101/2020.10.26.355420}, hal = {hal-03342409v1}, journal = {G3: Genes|Genomes|Genetics} }
Ecol. Lett.
Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of species abundances in fruit flies

Benoit Facon, Abir Hafsi, Maud Charlery Masselière, Stéphane Robin, François Massol, Maxime Dubart, Julien Chiquet, Enric Frago, Frédéric Chiroleu, Pierre-François Duyck, and Virginie Ravigné

Ecological Letters, 2021

Abs bioRxiv Bib

The relative importance of ecological factors and species interactions for phytophagous insect species distributions has long been a controversial issue. Using field abundances of eight sympatric Tephritid fruit flies on 21 host plants, we inferred flies’ realized niches using joint species distribution modelling and network inference, on the community as a whole and separately on three groups of host plants. These inferences were then confronted to flies’ fundamental niches estimated through laboratory-measured fitnesses on host plants. Species abundances were mainly determined by host plants followed by climatic factors, with a minor role for competition between species sharing host plants. The relative importance of these factors mildly changed when we focused on particular host plant groups. Despite overlapping fundamental niches, specialists and generalists had almost distinct realized niches, with possible competitive exclusion of generalists by specialists on Cucurbitaceae and different assembly rules: specialists were mainly influenced by their adaptation to host plants while generalist abundances varied regardless of their fundamental host use.Competing Interest StatementThe authors have declared no competing interest.
@article{Facon2021, author = {Facon, Benoit and Hafsi, Abir and de la Masseli{\`e}re, Maud Charlery and Robin, St{\'e}phane and Massol, Fran{\c c}ois and Dubart, Maxime and Chiquet, Julien and Frago, Enric and Chiroleu, Fr{\'e}d{\'e}ric and Duyck, Pierre-Fran{\c c}ois and Ravign{\'e}, Virginie}, title = {Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of species abundances in fruit flies}, year = {2021}, doi = {10.1111/ele.13825}, biorxiv = {early/2021/03/03/2020.12.07.414326}, journal = {Ecological Letters}, }
Microbiome
Temporal patterns in Ixodes ricinus microbial communities: an insight into tick-borne microbe interactions

E Lejal, J Chiquet, J Aubert, S Robin, A Estrada-Peña, O Rue, C Midoux, M Mariadassou, X Bailly, A Cougoul, P Gasqui, JF Cosson, K Chalvet-Monfray, M Vayssier-Taussat, and T Pollet

Microbiome, 2021

Abs bioRxiv Bib

Background Ticks transmit pathogens of medical and veterinary importance, and represent an increasing threat for human and animal health. Important steps in assessing disease risk and developing possible new future control strategies involve identifying tick-borne microbes, their temporal dynamics and interactions.Methods Using high throughput sequencing, we studied the microbiota dynamics of Ixodes ricinus from 371 nymphs collected monthly over three consecutive years in a peri-urban forest. After adjusting a Poisson Log Normal model to our data set, the implementation of a principal component analysis as well as sparse network reconstruction and differential analysis allowed us to assess inter-annual, seasonal and monthly variability of I. ricinus microbial communities as well as their interactions.Results Around 75% of the detected sequences belonged to five genera known to be maternally inherited bacteria in arthropods and potentially circulating in ticks: Candidatus Midichloria, Rickettsia, Spiroplasma, Arsenophonus and Wolbachia. The structure of the I. ricinus microbiota was temporally variable with interannual recurrence and seemed to be mainly driven by OTUs belonging to environmental genera. The total network analysis revealed a majority of positive (partial) correlations. We identified strong relationships between OTUs belonging to Wolbachia and Arsenophonus, betraying the presence of the parasitoid wasp Ixodiphagus hookeri in ticks, and the well known arthropod symbiont Spiroplasma, previously documented to be involved in the defense against parasitoid wasp in Drosophila melanogaster. Other associations were observed between the tick symbiont Candidatus Midichloria and pathogens belonging to Rickettsia, probably Rickettsia helvetica. More specific network analysis finally suggested that the presence of pathogens belonging to genera Borrelia, Anaplasma and Rickettsia might disrupt microbial interactions in I. ricinus.Conclusions Here, we identified the I. ricinus microbiota and documented for the first time the existence and recurrence of marked temporal shifts in the tick microbial community dynamics. We statistically showed strong relationships between the presence of some pathogens and the structure of the I. ricinus non-pathogenic microbes. We interestingly detected close links between some tick symbionts and the potential presence of either pathogenic Rickettsia or a parasitoid in ticks. All these new findings might be very promising for the future development of new control strategies of ticks and tick-borne diseases.Competing Interest StatementAuthor Cedric Midoux was employed by the company Irstea. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.I. ricinusIxodes ricinusPCAPrincipal Component AnalysisOTUOperational Taxonomic UnitTBPTick-Borne Pathogens
@article{Lejal2021, author = {Lejal, E and Chiquet, J and Aubert, J and Robin, S and Estrada-Pe{\~n}a, A and Rue, O and Midoux, C and Mariadassou, M and Bailly, X and Cougoul, A and Gasqui, P and Cosson, JF and Chalvet-Monfray, K and Vayssier-Taussat, M and Pollet, T}, title = {Temporal patterns in Ixodes ricinus microbial communities: an insight into tick-borne microbe interactions}, year = {2021}, volume = {9}, number = {153}, doi = {10.1186/s40168-021-01051-8}, biorxiv = {early/2020/09/26/2020.09.26.314179}, journal = {Microbiome}, }
Front Ecol Evol
The Poisson-Lognormal Model as a Versatile Framework for the Joint Analysis of Species Abundances

Julien Chiquet, Mahendra Mariadassou, and Stéphane Robin

Frontiers in Ecology and Evolution, 2021

Abs bioRxiv Bib

Joint Species Distribution Models (JSDM) provide a general multivariate framework to study the joint abundances of all species from a community. JSDM account for both structuring factors (environmental characteristics or gradients, such as habitat type or nutrient availability) and potential interactions between the species (competition, mutualism, parasitism, etc.), which is instrumental in disentangling meaningful ecological interactions from mere statistical associations. Modeling the dependency between the species is challenging because of the count-valued nature of abundance data and most JSDM rely on Gaussian latent layer to encode the dependencies between species in a covariance matrix. The multivariate Poisson-lognormal (PLN) model is one such model, which can be viewed as a multivariate mixed Poisson regression model. Inferring such models raises both statistical and computational issues, many of which were solved in recent contributions using variational techniques and convex optimization tools. The PLN model turns out to be a versatile framework, within which a variety of analyses can be performed, including multivariate sample comparison, clustering of sites or samples, dimension reduction (ordination) for visualization purposes, or inferring interaction networks. This paper presents the general PLN framework and illustrates its use on a series a typical experimental datasets. All the models and methods are implemented in the R package PLNmodels, available from cran.r-project.org.
@article{PLNfrontiers, author = {Chiquet, Julien and Mariadassou, Mahendra and Robin, Stéphane}, title = {The Poisson-Lognormal Model as a Versatile Framework for the Joint Analysis of Species Abundances}, journal = {Frontiers in Ecology and Evolution}, volume = {9}, pages = {188}, year = {2021}, doi = {10.3389/fevo.2021.588292}, issn = {2296-701X}, biorxiv = {10.1101/2020.10.07.329383v1}, }
J. Bio Comp Biol
Identification of deregulation mechanisms specific to cancer subtypes

Magali Champion, Julien Chiquet, Pierre Neuvial, Mohamed Elati, François Radvanyi, and Etienne Birmelé

Journal of Bioinformatics and Computational Biology, 2021

Abs HAL Bib

In many cancers, mechanisms of gene regulation can be severely altered. Identification of deregulated genes, which do not follow the regulation processes that exist between transcription factors and their target genes, is of importance to better understand the development of the disease. We propose a methodology to detect deregulation mechanisms with a particular focus on cancer subtypes. This strategy is based on the comparison between tumoral and healthy cells. First, we use gene expression data from healthy cells to infer a reference gene regulatory network. Then, we compare it with gene expression levels in tumor samples to detect deregulated target genes. We finally measure the ability of each transcription factor to explain these deregulations. We apply our method on a public bladder cancer data set derived from The Cancer Genome Atlas project and confirm that it captures hallmarks of cancer subtypes. We also show that it enables the discovery of new potential biomarkers.
@article{champion_2021, author = {Champion, Magali and Chiquet, Julien and Neuvial, Pierre and Elati, Mohamed and Radvanyi, François and Birmelé, Etienne}, title = {Identification of deregulation mechanisms specific to cancer subtypes}, journal = {Journal of Bioinformatics and Computational Biology}, volume = {19}, number = {01}, pages = {2140003}, year = {2021}, doi = {10.1142/S0219720021400035}, hal = {hal-03079966}, }

2020

Algo Mol Biol

Fast Computation of Genome-Metagenome interaction effects

Florent Guinot, Marie Szafranski, Julien Chiquet, Anouk Zancarini, Christine Le Signor, Christophe Mougel, and Christophe Ambroise

Algorithms for Molecular Biology, 2020

arXiv HAL Code

BMC Bioinfo

Fast tree aggregation for consensus hierarchical clustering

Audrey Hulot, Julien Chiquet, Florence Jaffrezic, and Guillem Rigaill

BMC Bioinformatics, 2020

Bib Code

@article{hulot_BMC_2020,
  author = {Hulot, Audrey and Chiquet, Julien and Jaffrezic, Florence and Rigaill, Guillem},
  title = {Fast tree aggregation for consensus hierarchical
                    clustering},
  journal = {BMC Bioinformatics},
  year = {2020},
  doi = {10.1186/s12859-020-3453-6},
}

2019

Cell

A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication

Maximilien Grandclaudon, Marie Perrot-Dockès, Coline Trichot, Omar Mostafa-Abouzid, Wassim Abou-Jaoudé, Frédérique Berger, Philippe Hupé, Denis Thieffry, Laure Sansonnet, Julien Chiquet, Céline Levy-Leduc, and Vassili Soumelis

Cell, 2019

Bib

@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},
  year = {2019},
  doi = {10.2139/ssrn.3353217},
}

ICML

Variational Inference for sparse network reconstruction from count data

Julien Chiquet, Stephane Robin, and Mahendra Mariadassou

In Proceedings of the 36th International Conference on Machine Learning, 09–15 jun 2019

PDF Code

JASA

Variational Inference for Stochastic Block Models from Sampled Data

Timothée Tabouy, Pierre Barbillon, and Julien Chiquet

Journal of the American Statistical Association, 09–15 jun 2019

arXiv Bib Code

@article{2019_JASA_TBC,
  author = {Tabouy, Timothée and Barbillon, Pierre and Chiquet, Julien},
  title = {Variational Inference for Stochastic Block Models
                    from Sampled Data},
  journal = {Journal of the American Statistical Association},
  volume = {0},
  number = {ja},
  pages = {1-20},
  year = {2019},
  publisher = {Taylor & Francis},
  doi = {10.1080/01621459.2018.1562934},
}

2018

Ann Appl Stat

Variational inference for probabilistic Poisson PCA

Julien Chiquet, Mahendra Mariadassou, and Stéphane Robin

Ann. Appl. Statist., 09–15 jun 2018

arXiv Bib Code

@article{2018_AOAS_cmr,
  author = {Chiquet, Julien and Mariadassou, Mahendra and Robin, St\'ephane},
  title = {Variational inference for probabilistic Poisson PCA},
  journal = {Ann. Appl. Statist.},
  fjournal = {The Annals of Applied Statistics},
  year = {2018},
  volume = {12},
  number = {4},
  pages = {2674-2698},
  issn = {1932-6157},
  doi = {10.1214/18-AOAS1177},
}

SAGMB

A multivariate variable selection approach for analyzing LC-MS metabolomics data

M. Perrot, C. Lévy-Leduc, Julien Chiquet, L. Sansonnet, M. Brégère, M.-P. Étienne, Stéphane Robin, and G. Genta-Gouve

SAGMB, 09–15 jun 2018

arXiv Code

JMVA

Variable selection in multivariate linear models with high-dimensional covariance matrix estimation

M. Perrot, C. Lévy-Leduc, L. Sansonnet, and Julien Chiquet

J. Multivar. Anal., 09–15 jun 2018

arXiv Bib Code

@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},
}

2017

arXiv

Sparsity by Worst-Case Penalties

Y. Grandvalet, Julien Chiquet, and C. Ambroise

09–15 jun 2017

arXiv Bib Code

@preprint{report_CSDA_chiquet,
  author = {Grandvalet, Y. and Chiquet, Julien and Ambroise, C.},
  title = {Sparsity by Worst-Case Penalties},
  journal = {arXiv preprint},
  year = {2017},
}

EJS

Efficient block boundaries estimation in block-wise constant matrices: An application to HiC data

Vincent Brault, Julien Chiquet, and Céline Lévy-Leduc

Elec. J. Statist., 09–15 jun 2017

arXiv Bib Code

@article{2017_EJS_BCLL,
  author = {Brault, Vincent and Chiquet, Julien and L\'evy-Leduc, C\'eline},
  title = {Efficient block boundaries estimation in block-wise
                    constant matrices: An application to HiC data},
  journal = {Elec. J. Statist.},
  fjournal = {Electronic Journal of Statistics},
  year = {2017},
  volume = {11},
  number = {1},
  pages = {1570-1599},
  doi = {10.1214/17-EJS1270},
}

JCGS

Fast tree inference with weighted fusion penalties

Julien Chiquet, Pierre Gutierrez, and Guillem Rigaill

Journal of Computational and Graphical Statistics, 09–15 jun 2017

arXiv Bib Code

@article{2016_JCGS_Chiquet,
  author = {Chiquet, Julien and Gutierrez, Pierre and Rigaill, Guillem},
  title = {Fast tree inference with weighted fusion penalties},
  journal = {Journal of Computational and Graphical Statistics},
  year = {2017},
  vol = {26},
  pages = {205--216},
  doi = {10.1080/10618600.2015.1096789},
}

2016

Stat Model

Categorical regularization: Discussion of article by Tutz and Gertheiss

Julien Chiquet, Y. Grandvalet, and G. Rigaill

Statistical Modelling, 09–15 jun 2016

HAL Bib

@article{2016_SM_Chiquet,
  author = {Chiquet, Julien and Grandvalet, Y. and Rigaill, G.},
  title = {Categorical regularization: Discussion of article by
                    Tutz and Gertheiss},
  year = {2016},
  vol = {16},
  pages = {228-237},
  number = {3},
  journal = {Statistical Modelling},
  doi = {10.1177/1471082X16644998},
  hal = {hal-01338164},
}

Stat Comput

Structured Regularization for conditional Gaussian Graphical Models

Julien Chiquet, T. Mary-Huard, and S. Robin

Statistics and Computing, 09–15 jun 2016

arXiv Bib Code

@article{2016_SC_Chiquet,
  author = {Chiquet, Julien and Mary-Huard, T. and Robin, S.},
  title = {Structured Regularization for conditional {Gaussian}
                    Graphical Models},
  journal = {Statistics and Computing},
  year = {2016},
  vol = {27},
  number = {3},
  pages = {789-804},
  doi = {10.1007/s11222-016-9654-1},
}

Adv Ecol Res

Learning ecological networks from next-generation sequencing data

Corinne Vacher, Alireza Tamaddoni-Nezhad, Stefaniya Kamenova, Nathalie Peyrard, Yann Moalic, Regis Sabbadin, Loı̈c Schwaller, Julien Chiquet, M Alex Smith, Jessica Vallance, and others

In Advances in Ecological Research, 09–15 jun 2016

Bib

@incollection{2016_AER_Vacher,
  title = {Learning ecological networks from next-generation
                    sequencing data},
  author = {Vacher, Corinne and Tamaddoni-Nezhad, Alireza and Kamenova, Stefaniya and Peyrard, Nathalie and Moalic, Yann and Sabbadin, Regis and Schwaller, Lo{\"\i}c and Chiquet, Julien and Smith, M Alex and Vallance, Jessica and others},
  booktitle = {Advances in Ecological Research},
  volume = {54},
  pages = {1--39},
  year = {2016},
  doi = {10.1016/bs.aecr.2015.10.004},
  publisher = {Elsevier},
}

JMVA

Combining a Relaxed EM Algorithm with Occam’s Razor for Bayesian Variable Selection in High-Dimensional Regression

Pierre Latouche, Pierre-Alexandre Mattei, Charles Bouveyron, and Julien Chiquet

Journal of Multivariate Analysis, 09–15 jun 2016

HAL Bib Code

@article{2016_JMVA_Latouche,
  author = {Latouche, Pierre and Mattei, Pierre-Alexandre and Bouveyron, Charles and Chiquet, Julien},
  title = {Combining a Relaxed {EM} Algorithm with {Occam's}
                    Razor for {Bayesian} Variable Selection in
                    High-Dimensional Regression},
  journal = {Journal of Multivariate Analysis},
  year = {2016},
  page = {177-190},
  vol = {146},
  hal = {hal-01003395v2},
  doi = {10.1016/j.jmva.2015.09.004},
}

2015

BMC Syst Biol

A model for gene deregulation detection using expression data

Thomas Picchetti, Julien Chiquet, Mohamed Elati, Pierre Neuvial, Rémy Nicolle, and Etienne Birmelé

BMC Systems Biology, 09–15 jun 2015

HAL Bib

@article{picchetti:hal-01154154,
  title = {A model for gene deregulation detection using
                    expression data},
  author = {Picchetti, Thomas and Chiquet, Julien and Elati, Mohamed and Neuvial, Pierre and Nicolle, R{\'e}my and Birmel{\'e}, Etienne},
  journal = {BMC Systems Biology},
  year = {2015},
  vol = {9},
  hal = {hal-01154154},
  doi = {10.1186/1752-0509-9-S6-S6},
}

2014

Science

Early allopolyploid evolution in the post-neolithic Brassica napus oilseed genome

B. Chaloub, F. Denoeud, S. Liu, S. Parkin, H. Tang, Wang X., Julien Chiquet, and 76 more

Science, 09–15 jun 2014

Bib

@article{2014_Science_Chaloub,
  author = {Chaloub, B. and Denoeud, F. and Liu, S. and Parkin, S. and Tang, H. and X., Wang and Chiquet, Julien and {76 more}},
  title = {Early allopolyploid evolution in the post-neolithic
                    {Brassica} napus oilseed genome},
  journal = {Science},
  year = {2014},
  vol = {345},
  number = {6199},
  page = {950--953},
  doi = {10.1126/science.1253435},
}

2013

New Phytol

Prevalence of gene expression additivity in genetically stable wheat allohexaploids

H. Chelaifa, V. Chagué, S. Chalabi, I. Mestiri, D. Arnaud, D. Deffains, Y. Lu, H. Belcram, V. Huteau, Julien Chiquet, O. Coriton, J. Just, J. Jahier, and B. Chalhoub

New Phytologist, 09–15 jun 2013

Bib

@article{2013_NP_Chiquet,
  author = {Chelaifa, H. and Chagué, V. and Chalabi, S. and Mestiri, I. and Arnaud, D. and Deffains, D. and Lu, Y. and Belcram, H. and Huteau, V. and Chiquet, Julien and Coriton, O. and Just, J. and Jahier, J. and Chalhoub, B.},
  title = {Prevalence of gene expression additivity in
                    genetically stable wheat allohexaploids},
  journal = {New Phytologist},
  volume = {197},
  number = {3},
  issn = {1469-8137},
  pages = {730--736},
  year = {2013},
  doi = {10.1111/nph.12108/full},
}

2012

Ann Appl Stat

Sparsity in sign-coherent groups of variables via the cooperative-Lasso

Julien Chiquet, Y. Grandvalet, and C. Charbonnier

The Annals of Applied Statistics, 09–15 jun 2012

arXiv Bib

@article{2012_AAS_chiquet,
  author = {Chiquet, Julien and Grandvalet, Y. and Charbonnier, C.},
  title = {Sparsity in sign-coherent groups of variables via
                    the cooperative-Lasso},
  journal = {The Annals of Applied Statistics},
  year = {2012},
  volume = {6},
  number = {2},
  pages = {795--830},
  doi = {10.1214/11-AOAS520},
}

2011

Stat Comput

Inferring multiple graphical models

Julien Chiquet, Y. Grandvalet, and C. Ambroise

Statistics and Computing, 09–15 jun 2011

arXiv Bib Code

@article{2011_SC_Chiquet,
  author = {Chiquet, Julien and Grandvalet, Y. and Ambroise, C.},
  title = {Inferring multiple graphical models},
  journal = {Statistics and Computing},
  year = {2011},
  volume = {21},
  number = {4},
  pages = {537--553},
  doi = {10.1007/s11222-010-9191-2},
}

2010

SAGMB

Weighted-Lasso for structured network inference from time course data

C. Charbonnier, Julien Chiquet, and C. Ambroise

Statistical Applications in Genomics and Molecular Biology, 09–15 jun 2010

arXiv Bib Code

@article{2010_SAGMB_Chiquet,
  author = {Charbonnier, C. and Chiquet, Julien and Ambroise, C.},
  title = {Weighted-Lasso for structured network inference from
                    time course data},
  journal = {Statistical Applications in Genomics and Molecular
                    Biology},
  year = {2010},
  volume = {9},
  doi = {1544-6115.1519},
}

2009

EJS

Inferring sparse Gaussian graphical models with latent structure

C. Ambroise, Julien Chiquet, and C. Matias

Electronic Journal of Statistics, 09–15 jun 2009

arXiv Bib Code

@article{2009_EJS_Chiquet,
  author = {Ambroise, C. and Chiquet, Julien and Matias, C.},
  title = {Inferring sparse {Gaussian} graphical models with
                    latent structure},
  journal = {Electronic Journal of Statistics},
  year = {2009},
  volume = {3},
  pages = {205--238},
  doi = {10.1214/08-EJS314},
}

Bioinformatics

SIMoNe: Statistical Inference for MOdular NEtworks

Julien Chiquet, A. Smith, G. Grasseau, C. Matias, and C. Ambroise

Bioinformatics, 09–15 jun 2009

Bib Code

@article{2009_BI_Chiquet,
  author = {Chiquet, Julien and Smith, A. and Grasseau, G. and Matias, C. and Ambroise, C.},
  title = {{SIMoNe}: Statistical Inference for MOdular
                    NEtworks},
  journal = {Bioinformatics},
  year = {2009},
  volume = {25},
  number = {3},
  pages = {417--418},
  doi = {10.1093/bioinformatics/btn637},
}

JSPI

Piecewise deterministic Markov processes applied to fatigue crack growth modelling

Julien Chiquet, N. Limnios, and M. Eid

Journal of statistical planning and inference, 09–15 jun 2009

Bib

@article{2009_JSPI_Chiquet,
  author = {Chiquet, Julien and Limnios, N. and Eid, M.},
  title = {Piecewise deterministic {Markov} processes applied
                    to fatigue crack growth modelling},
  journal = {Journal of statistical planning and inference},
  year = {2009},
  volume = {139},
  number = {5},
  pages = {1657--1667},
  doi = {10.1016/j.jspi.2008.05.034},
}

2008

Stat Prob Lett

A method to compute the transition function of a piecewise deterministic Markov process

Julien Chiquet, and N. Limnios

Statistics & Probability Letters, 09–15 jun 2008

Bib

@article{2008_SPL_Chiquet,
  author = {Chiquet, Julien and Limnios, N.},
  title = {A method to compute the transition function of a
                    piecewise deterministic {Markov} process},
  journal = {Statistics \& Probability Letters},
  year = {2008},
  volume = {78},
  number = {12},
  pages = {1397--1403},
  doi = {10.1016/j.spl.2007.12.016},
}

RESS

Modelling and Estimating Stochastic Dynamical Systems with Markovian Switching

Julien Chiquet, N. Limnios, and M. Eid

Reliability Engineering System Safety, 09–15 jun 2008

Bib

@article{2008_RESS_Chiquet,
  author = {Chiquet, Julien and Limnios, N. and Eid, M.},
  title = {Modelling and Estimating Stochastic Dynamical
                    Systems with {Markovian} Switching},
  journal = {Reliability Engineering System Safety},
  year = {2008},
  volume = {93},
  number = {12},
  pages = {1801--1808},
  doi = {10.1016/j.ress.2008.03.016},
}

2006

MCAP

Estimating stochastic dynamical systems driven by a continuous-time jump Markov process

Julien Chiquet, and N. Limnios

Methodology and Computing in Applied Probability, 09–15 jun 2006

HAL Bib

@article{2006_MCAP_Chiquet,
  author = {Chiquet, Julien and Limnios, N.},
  title = {Estimating stochastic dynamical systems driven by a
                    continuous-time jump {Markov} process},
  journal = {Methodology and Computing in Applied Probability},
  year = {2006},
  volume = {8},
  hal = {cea-02360117/},
  pages = {431--447},
  url = {http://www.springerlink.com/content/e8736480p20271l3/},
}

Statistical Approaches for Hidden Variables in Ecology

Julien Chiquet, Marie-Josée Cros, Mahendra Mariadassou, Nathalie Peyrard, and Stéphane Robin

2022
A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer

J. Chiquet, R. Rigaill, and M. Sundqvist

2019

Code
Probabilistic graphical models dedicated to applications in genetics, genomics and postgenomics

M. Jeanmoungin, C. Charbonnier, M. Guedj, and J. Chiquet

2014
Stochastic Reliability and Maintenance Modeling

J. Chiquet, and N. Limnios

2013
Mathematical methods in survival analysis, reliability and quality of life

J. Chiquet, and N. Limnios

2008

Contributions to sparse methods for complex data analysis

J. Chiquet

2015

PDF
Modélisation et estimation des processus de dégradation avec application en fiabilité des structures

J. Chiquet

Université de Technologie de Compiègne, 2007

PDF
Estimation des températures journalières à l’aide de techniques markoviennes

J. Chiquet

Université de Technologie de Compiègne, 2003

PDF

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