- 11
- 7
- 2
- 1
- 1
DC
David Causeur
22
Documents
Identifiants chercheurs
- david-causeur
- 0000-0001-6910-9440
- Google Scholar : https://scholar.google.com/citations?user=kCws61IAAAAJ&hl=fr
Présentation
Research topics
===============
My research interests are in statistical methodology for various issues motivated by biological applications. More particularly, the focus of my recent papers is on the handling of dependence in high-dimensional statistical inference.
**Statistical genomics**
Dependence within high-dimensional gene expression profiles generates a strong instability of gene selection in large scale significance analysis. A proper handling of this dependence by latent factor models or more general whitening techniques improves stability of multiple testing procedures and power (see for example Friguet *et al*, 2009 \[JASA\], Friguet and Causeur, 2010 \[CSDA\], Causeur *et al*, 2011 \[JSS\], Hornung *et al*, 2016 \[BMC Bioinf.\], Hornung *et al*, 2017 \[Bioinf.\], Hébert *et al*, 2021 \[CSDA\]).
**Functional data analysis**
Functional data are discretized observations of curves. Such data are generated by various technologies such as spectroscopy or electroencephalography (EEG). More and more study designs, such as Event-Related Potentials studies in neuroscience, aim at assessing the relationship between functional data and experimental covariates. Both for signal detection (global testing) and signal identification (search for significant intervals), dependence handling strategies can be designed to be efficient for a given pattern of association signal (see for example Causeur *et al*, 2012 \[BRM\], Sheu *et al*, 2016 \[AoAS\], Causeur *et al*, 2020 \[Biometrics\]).
**High-dimensional regression and classification modeling**
Both in genomic and functional data analysis, estimation of regression and classification models has to deal with a possibly strong dependence within high-dimensional profiles of explanatory variables. Whitening procedures can help stabilizing model selection methods and improve prediction performance (see for example Perthame *et al*, 2015 \[StatCo\], Hébert *et al*, 2021 \[Under revision\]).
**Biostatistics**
I am involved in various research projects with a diversity of partners in biology, recently for multi-omic data integration issues (see Gondret *et al*., 2017 \[BMC Gen.\], Désert *et al*., 2018 \[BMC Gen.\]), peptidomics (see Suwareh *et al*., 2021 \[Food Ch.\]), electromyographic data analysis (see Comfort *et al*., 2021 \[PACA\]), etc.
Publications
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 22
- 12
- 10
- 6
- 5
- 5
- 4
- 4
- 4
- 4
- 4
- 3
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 4
- 1
- 1
- 1
- 2
- 1
- 1
- 2
- 5
- 5
- 5
Identification des fonctions biologiques, spécifiques d’un tissu ou partagées entre tissus, associées aux différences d’efficacité alimentaire chez le porc en croissance49. Journées de la Recherche Porcine, Jan 2017, Paris, France. pp.13-18
Communication dans un congrès
hal-01602699v1
|
|
|
Sparse factor model for gene co-expression networksBioNetVisA Workshop, Sep 2014, Strasbourg, France
Communication dans un congrès
hal-01210988v1
|
|
Inferring gene networks using a sparse factor model approachStatistical learning and data science, May 2012, Florence, Italy. 12 p
Communication dans un congrès
hal-02806777v1
|
|
Inferring gene networks using a sparse factor model approach, Statistical Learning and Data ScienceLearning and Data Science, May 2012, Florence (IT), Italy
Communication dans un congrès
hal-00841017v1
|
Genetic analysis of a complex trait using transcriptomic data: contribution of gene regulatory network modelingXX. Conference of the plant & animal genome (PAG), Jan 2012, San Diego, United States. 1 p
Communication dans un congrès
hal-00841103v1
|
|
Effect of the gene diacylglycerol-O-transferase 1 (DGAT1) polymorphism on the global expression pattern of genes in the mammary gland tissue of dairy cows62. Annual Meeting of the European Federation of Animal Science (EAAP), Aug 2011, Stavanger, Norway
Communication dans un congrès
hal-02747109v1
|
|
|
Integrating QTL controlling fatness, lipid metabolites and gene expressions to genetically dissect the adiposity complex trait in a meat chicken cross62. Annual Meeting of the European Association for Animal Production (EAAP), Aug 2011, Stavanger, Norway
Communication dans un congrès
hal-02748764v1
|
|
Large-scale significance testing of high thoroughput Data with FAMT14. Conference of the Applied Stochastic Models and Data Analysis International Society, Jun 2011, Rome, Italy
Communication dans un congrès
hal-02746752v1
|
|
Transcriptome profiling reveals interaction between two QTL for fatness in chicken15th European workshop on QTL mapping and marker assisted selection (QTLMAS), May 2011, Rennes (FR), France. 1 p
Communication dans un congrès
hal-00841016v1
|
|
Inférence sur réseaux géniques par Analyse en Facteurs42èmes Journées de Statistique, 2010, Marseille, France, France
Communication dans un congrès
inria-00494802v1
|
A factor model to analyze heterogeneity in gene expression in a context of QTL mapping8th workshop " Statistical Methods for Post-Genomic Data ", Jan 2010, Marseille, France
Communication dans un congrès
hal-00459362v1
|
|
Integrative responses of pig adipose tissues to high-fat high-fiber diet: towards key regulators of energy flexibilityASAS/ADSA midwest meeting, Mar 2015, Des Moines, United States. Journal of Animal Science, 93 (Suppl. 2), 2015, Abstract book of the ASAS/ADSA midwest meeting
Poster de conférence
hal-01210925v1
|
|
The R package FANet: sparse factor analysis model for high dimensional gene co-expression networksThe International R Users Conference, Jun 2014, Los Angeles, United States. 2014, UserR contributed abstracts
Poster de conférence
hal-01211038v1
|
Genetic analysis of a complex trait using transcriptomic data: contribution of gene regulatory network modelingXX. Conference of the plant & animal genome (PAG), Jan 2012, San Diego, United States. 2012
Poster de conférence
hal-02806842v1
|
|
|
A factor model to analyse heterogeneity in gene expression in a context of QTL characterizationXXXII International Conference on Animal Genetics (ISAG), Jul 2010, Edinburgh, United Kingdom. 2010, Proceedings of the 32nd International Conference on Animal Genetics
Poster de conférence
hal-02754275v1
|
|
Transcriptome profiling reveals interaction between two QTL for fatness in chickenXXXII International Conference on Animal Genetics (ISAG), Jul 2010, Edinburgh, United Kingdom. 2010, Proceedings of the 32nd International Conference on Animal Genetics
Poster de conférence
hal-02754276v1
|