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INRA
24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal AgroParisTech Université Paris-Saclay

INRA GABI Unit

GABI : Génétique Animale et Biologie IntégrativeUnité Mixte de Recherche INRA - AgroParisTech

Andrea RAU, Junior Research Scientist

ANDREA RAU, INRA UMR GABI
Gene regulatory networks, analysis of high-throughput sequencing data (RNA-seq), mixture models, Bayesian analysis, approximate Bayesian computation (ABC), analysis of genomic and transcriptomic data

INRA UMR 1313 Génétique Animale et Biologie Intégrative
Domaine de Vilvert, Bat 211, 78352 Jouy en Josas
Phone : +33 (0) 1 34 65 22 82 Fax : +33 (0) 1 34 65 22 10

Email: andrea.rau(at)jouy.inra.fr

Team : Population, Statistics, and Genome (PSGen)

http://andrea-rau.com

CV

Since 2011: Junior research scientist, INRA (Jouy-en-Josas, France)
2010 – 2011: Post-doctoral researcher, Inria Saclay – Île-de-France (Orsay, France)
2007 – 2010: Ph.D. in Statistics, Purdue University (West Lafayette, Indiana, USA)
2005 – 2007: M.S. (Master of Science) in Applied Statistics, Purdue University (West Lafayette, Indiana, USA)
2001 – 2005: B.A. (Bachelor of Arts) in French and B.A. in Mathematics with a concentration in Statistics, Saint Olaf College (Northfield, Minnesota, USA)

Fields of research :

Gene regulatory networks, analysis of high-throughput sequencing data (RNA-seq), mixture models, Bayesian analysis, approximate Bayesian computation (ABC), analysis of genomic and transcriptomic data

Other activities :

Recent Publications and other Productions

Statistics methods

  1. Monneret, G., Jaffrézic, F., Rau, A., Nuel, G. (2015). Estimation d’effets causaux dans les réseaux de régulation génique : vers la grande dimension. Revue d’intelligence articielle, accepted.
  2. Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux, G. (2015) Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, doi: 10.1093/bioinformatics/btu845.
  3. Rau, A., Marot, G. and Jaffrézic, F. (2014) Differential meta-analysis of RNA-seq data from multiple studies. BMC Bioinformatics, 15:91.
  4. Nuel, G., Rau, A., and Jaffrézic, F. (2013) Using pairwise ordering preferences to estimate causal effects in gene expression from a mixture of observational and intervention experiments. Quality Technology and Quantitative Management 11(1):23-37.
  5. Rau, A., Jaffrézic, F., and Nuel, G. (2013) Joint estimation of causal effects from observational and intervention gene expression data. BMC Systems Biology 7:111.
  6. Gallopin, M. Rau, A., and Jaffrézic, F. (2013). A hierarchical Poisson log-normal model for network inference from RNA sequencing data. PLoS One 8(10): e77503.
  7. Rau, A., Gallopin, M., Celeux, G., and Jaffrézic, F. (2013). Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics 29(17): 2146-2152.
  8. Dillies, M.-A.*, Rau, A.*, Aubert, J.*, Hennequet-Antier, C.*, Jeanmougin, M.*, Servant, N.*, Keime, C.*, Marot, G., Castel, D., Estelle, J., Guernec, G., Jagla, B., Jouneau, L., Laloë, D., Le Gall, C., Schaëffer, B., Charif, D., Le Crom, S.*, Guedj, M.*, and Jaffrézic, F*. (2012). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in Bioinformatics (in press). doi:10.1093/bib/bbs046. *These authors contributed equally to this work.
  9. Rau, A., Jaffrézic, F., Foulley, J.-L., and Doerge, R. W. (2012). Reverse engineering gene regulatory networks using approximate Bayesian computation. Statistics and Computing, 22: 1257-1271.
  10. Rau, A., Jaffrézic, F., Foulley, J.-L., and Doerge, R. W. (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Statistical Applications in Genetics and Molecular Biology: Vol. 9: Iss. 1, Article 9.

Statistics applications

  1. Endale Ahanda, M.-L., Zerjal, T., Dhorne-Pollet, S., Rau, A., Cooksey, A., and Giuffra, E. (2014) Impact of the genetic background on the composition of the chicken plasma miRNome in response to a stress. PLoS One, 9(12): e114598.
  2. Brenault, P., Lefevre, L. Rau, A., Laloë, D., Pisoni, G., Moroni, P., Bevilacquia, C. and Martin, P. (2013) Contribution of mammary epithelial cells to the immune response during early stages of a bacterial infection to Staphylococcus aureus. Veterinary Research 45:16.
  3. Furth, A., Mandrekar, S., Tan, A. Rau, A., Felten, S., Ames, M. Adjei, A. Erlichman, C. and Reid, J. (2008). A limited sample model to predict area under the drug concentration curve for 17-(allylamino)-17-demethoxygeldanamycin and its active metabolite 17-(amino)-17-demethoxygeldanomycin. Cancer Chemotherapy Pharmacology 61(1): 39-45.

Book

Albert, I., Ancelet, S., David, O., Denis, J.-B., Makowski, D., Parent, É., Rau, A., and Soubeyrand, S. (expected 2015).Initiation à la statistique bayésienne : Bases théoriques et applications en alimentation, environnmenet, épidémiologie et génétique : Éditions Ellipses, collection références sciences.