Hub members Have many expertise, covering most of the fields in bioinformatics and biostatistics. You'll find below a non-exhaustive list of these expertise
Searched keyword : Statistical inference
Related people (8)
I obtained an engineering degree in Biomedical engineering from Université de Technologie de Compiègne (UTC) in 1989, a master degree in Control of Complex Systems from UTC in 1990, a PhD in Control of Complex Systems from UTC in 1993, a University Degree in Human Genetics from The University of Rennes 1 in 2001 and a master degree in Functional Genomics from University Paris Diderot (Paris 7) in 2002. I worked as a statistician at the Transcriptome and Epigenome Platform from 2002 to 2017, where I was responsible for the statistical analyses of the data and had an important training activity (on the campus and outside). Since 2015 I have been co-head of the Bioinformatics and Biostatistics Hub within the Center of Bioinformatics, Biostatistics and Integrative Biology (C3BI). I am co-director of the Pasteur course Introduction to Data Analysis and co-organiser of the sincellTE summer school (a school dedicated to single cell transcriptome and epigenome data analysis). I am also co-managing the StatOmique group which gathers more than 60 statisticians from France.
RNA-seqStatistical inferenceTranscriptomicsBiostatisticsApplication of mathematics in sciencesExploratory data analysisIllumina HiSeqStatistical experiment designSequencing
- Biomarqueurs d’identification précoce du sepsis aux urgences (BIPS)(Jean-Marc CAVAILLON - Cytokines and Inflammation) - In Progress
- Study of the early pathogenesis during Lassa fever in cynomolgus monkeys and its correlation with the outcome(Sylvain BAIZE - Biology of Viral Emerging Infections) - In Progress
- Host microbiota modification by the pathogen Listeria monocytogenes(Javier PIZARRO-CERDA - Bacteria-Cell Interactions) - Closed + 1 project
Since September 2016, I am a research engineer in the Bioinformatics and Biostatistics HUB of the Institut Pasteur and detached in the Proteomics facility. I have a PhD in Signal Processing from the Ecole Nationale Supérieure des Télécommunications de Bretagne (Telecom Bretagne) and a Master in Mathematics with a specialty in Statistical Engineering from Rennes 1 University. After my PhD, I was a research and teaching assistant in Mathematics at the Institut National des Sciences Appliquées (INSA) of Rennes, then I worked as a consultant for public local authorities in the company Ressources Consultants Finances. I started working in the field of Proteomics in October 2014 in the EDyP laboratory located in Grenoble (http://www.edyp.fr/). I have been working on the improvement of statistical analysis of bottom-up proteomics data. Today, most of the projects I work on consist of detecting changes in protein abundances using discovery-driven mass spectrometry. I am interested in the development of new methodologies to optimize proteomics data analysis pipelines, from the identification of peptides/proteins to their quantification and the interpretation of results. For this purpose, I worked on several R packages which can be downloaded from the CRAN and Bioconductor: cp4p (https://cran.r-project.org/web/packages/cp4p/index.html), imp4p (https://cran.r-project.org/web/packages/imp4p/index.html), DAPAR (http://bioconductor.org/packages/release/bioc/html/DAPAR.html) and its GUI ProStar.
Machine learningModelingPathway AnalysisProteomicsStatistical inferenceBiostatisticsApplication of mathematics in sciencesData and text miningData integrationStatistical experiment designMultidimensional data analysis
Data VisualizationMachine learningStatistical inferenceBiostatisticsApplication of mathematics in sciencesDimensional reductionMultidimensional data analysis
- Optimisation of freeze and conservation method of peripherical blood mononucleated cells(SORDOILLET VALLIER - Other) - Pending
- Afribiota-Neuro(Pascale VONAESCH - Molecular Microbial Pathogenesis) - In Progress
- Genetic and statistical analysis of data produced with the Collaborative Cross at the Institut Pasteur(Xavier MONTAGUTELLI - Mouse Genetics) - In Progress
Data managementMachine learningStatistical inferenceScientific computingExploratory data analysisSofware development and engineeringParallel computingNeuroimaging and computational neuroscienceGrid and cloud computing
Image analysisStatistical inferenceBiostatisticsGeneticsSequencingDiagnostic tools
- The anti-IgE antibody Omalizumab induces adverse reactions through engagement of Fc gamma receptors(Bianca BALBINO - Antibodies in Therapy and Pathology) - In Progress
- LGP2 binds PACT to regulate RIG-I- and MDA5-mediated antiviral response(Anastassia KOMAROVA - Viral Genomics and Vaccination) - In Progress
- Comparison of matrices of antibody gene usage(Pierre BRUHNS - Antibodies in Therapy and Pathology) - Pending
Since February 2017 Research engineer, Hub of Bioinformatics and Biostatistics of the C3BI, Institut Pasteur 2015-2017 Post doctoral position, team MISTIS, INRIA Grenoble Topic: Robust clustering and robust non linear regression in high dimension. Collaboration with Florence Forbes (INRIA). 2012-2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes Topic: Stability of variable selection in regression and classification issues for correlated data in high dimension. Supervisor: David Causeur (Agrocampus-Ouest, IRMAR). Education 2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes 2012 ISUP degree (Institut de Statistique de l’UPMC), Université Pierre et Marie Curie, Paris 2012 Master 2 of Statistics, Université Pierre et Marie Curie, Paris
ClusteringModelingStatistical inferenceTranscriptomicsBiostatisticsExploratory data analysisDimensional reductionStatistical experiment designMultidimensional data analysis
- Quality controls for human plasmas and serums stored in biobanks(HELENE LAUDE - Biological Resource Center of Institut Pasteur (CRBIP)) - In Progress
- Early transcriptional signature of T-cell memory after dengue vaccination(Claude ROTH - Functional Genetics of Infectious Diseases) - Pending
- Determination of host response elicited by different Salmonella lifestyles(Chak Hon LUK - Dynamics of Host-Pathogen Interactions) - Pending
Hugo Varet is a biostatistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and has been recruited by the hub of the C3BI (Center of Bioinformatics, Biostatistics and Integrative Biology) to work at the Transcriptome & Epigenome Platform. He is in charge of the statistical analyses of the RNA-Seq data produced by the platform and develops R pipelines that help in this task. One of them is named SARTools and is available on GitHub: https://github.com/PF2-pasteur-fr/SARTools.
ModelingSequence analysisStatistical inferenceTranscriptomicsBiostatisticsScientific computingApplication of mathematics in sciencesExploratory data analysisHigh Throughput ScreeningClinical research
- Defining Shigella-targeting of human lamina propria mononuclear cells using CyTOF technology(Katja BRUNNER - Molecular Microbial Pathogenesis) - Closed
- Exploring immunological mechanisms of human graft-verus-host disease after hematopoietic stem cell transplantation(Eleonora LATIS - Immunoregulation) - Closed
- Functional interactions between HP1 proteins and RNA.(Christophe RACHEZ - Epigenetic Regulation) - Closed
After a diploma of statistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and a Ph.D in applied mathematics in the Statistics & Genome lab (AgroParisTech), I worked as a developer for the XLSTAT software. I have implemented some statistical methods such as mixture models, log-linear regression, mood test, bayesian hierarchical modeling CBC/HB, … Then I worked as a head teacher in statistics for one year. I was recruited in the Bioinformatic and biostatistic hub of the C3BI (Center of Bioinformatics, Biostatistics and Integrative Biology) in 2014, I am in charge of the statistical analysis and the development of R/R shiny pipelines.
Machine learningStatistical inferenceTargeted metagenomicsBiostatisticsApplication of mathematics in sciencesStatistical experiment design
Related projects (8)
Invasion of epithelial cells by the obligate intracellular bacterium Chlamydia trachomatis results in its enclosure inside a membrane-bound compartment termed an inclusion. The bacterium quickly begins manipulating interactions between host intracellular trafficking and the inclusion interface, diverging from the endocytic pathway and escaping lysosomal fusion. We have isolated a mutant strain that shows several developmental defects. The C3BI will contribute to the statistical analysis of the data.
Notre but est de trouver un test statistique capable de dire si deux matrices de nombres sont différents. Ces matrices correspondent à l’utilisation de fragments de gènes (V ou J) pour créer un gène ré-arrangé (V-J) codant pour un anticorps de spécificité particulière. Ici nous avons représenté pour chaque gène V la famille de gène J utilisé (parmi J1, J2, J3, J4). Le nombre correspondant au nombre d’occurrences que nous avons trouvé après séquençage du répertoire d’anticorps spécifiques à partir de souris immunisées contre l’un de ces antigènes. Les matrices ont été réalisées pour les deux chaines codant la spécificité d’un anticorps: la chaine lourde (VH) ou la chaine légère (VL). Le but est de comparer les deux matrices VH l’une avec l’autre, et les deux matrices VL l’une avec l’autre.
The present work is to systematically investigate the role of TLRs and NODs in the host defense during C. trachomatis infection using KO animals. The inflammatory cytokines and bacterial burden will be measured using Bio-Plex ELISA kit. C3BI will provide help in the data analysis.
Non human primates are an important reservoir for zoonotic disease. Here we analyze in Cameroon how human activities in the forest influence contact with non human primates to better understand processes of emergence.
Quantitatively understanding the stochastic dynamics of gene expression requires measurements at the level of single cells. A common approach to follow the expression of genes in single cells and in real time is to make use of fluorescent reporter proteins and to record the cells' fluorescence by microscopy. However, this provides only an indirect readout of the biological processes that are of interest such as the regulation mechanisms at the promoter. A possible way to uncover the unobservable biological processes is to infer the hidden dynamics from the available data through the use of mechanistic models of gene expression. The goal of this project is to develop methods for state estimation and parameter inference for such models and to test these methods on real data.
Réalisation d'un interactome entre des protéines de mammifères et une protéine bactérienne de virulence.
Mood disorders such as bipolar and major depressive illnesses are among the most severe psychiatric disorders. They have high prevalence and chronic course, and are associated with significant mental and somatic comorbidities and high personal and societal costs (lost productivity and increased medical expenses). Patients with bipolar disorder (BD), for example, exhibit a reduced lifespan compared with the general population, a finding that cannot only be explained by high suicide risk, reduced access to medical care and lifestyle factors. However, the pathophysiological mechanisms of BD are poorly understood, and patients often have incomplete treatment response. Advanced mathematical approaches such as machine learning techniques are increasingly being used to generate predictions based on complex data, and it has been successfully used to detect a number of clinical outcomes and to predict behaviours. In combination with mobile technologies (e.g. smartphones, wearables) to collect behavioural, physiological and environmental data, these big data predictive approaches may provide a much richer and deeper understanding of phenomenology and pathophysiological mechanisms of mood and bipolar disorders. By taking advantage of the high-standard bioinformatics expertise offered by the C3BI, this multidisciplinary, collaborative project aims to explore how clinical and biological factors, may contribute for better characterizing BD patients as well as to identify predictors of treatment response in BD. Our project also aims to explore how daily behavioural and physiological parameters may influence mood and behaviour in individuals at-risk or suffering from mood disorders.
Our goal is to have a bioinformatic tool that performs the 2 x 2 comparison of matrices of numbers in matrix batches. Each matrix corresponds to the use of gene fragments (V or J) to create a re-arran