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


  1. Rau, A. (2017) Statistical methods and software for the analysis of transcriptomic data. HDR (Habilitation à diriger des recherches) thesis, Université d’Évry Val-d’Essonne. [link] Note: HDR is a high-level (post-PhD) degree granted by French universities that provides an accreditation to supervise research.
  2. Rau, A. (2010) Reverse engineering gene networks using genomic time-course data. PhD thesis, Purdue University. [link]

Statistical methods

  1. Rau, A., Flister, M. J., Rui, H. and Livermore Auer, P. (2018, accepted) Exploring drivers of gene expression in The Cancer Genome Atlas. Bioinformatics, doi: https://doi.org/10.1101/227926 [link]
  2. Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2018) Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. Journal of Applied Statistics, https://doi.org/10.1080/02664763.2018.1454894. [link]
  3. Rau, A. and Maugis-Rabusseau, C. (2018) Transformation and model choice for RNA-seq co-expression analysis. Briefings in Bioinformatics, bbw128, https://doi.org/10.1093/bib/bbw128
  4. Monneret, G., Jaffrézic, F., Rau, A., Zerjal, T. and Nuel, G. (2017) Identification of marginal causal relationships in gene networks from observational and interventional expression data. PLoS One 12(3): e0171142. [link]
  5. Rigaill, G., Balzergue, S., Brunaud, V., Blondet, E., Rau, A., Rogier, O., Caius, J.,  Maugis-Rabusseau, C., Soubigou-Taconnat, L., Aubourg, S., Lurin, C., Martin-Magniette, M.-L., and Delannoy, E. (2016) Synthetic datasets for the identification of key ingredients for RNA-seq differential analysis. Briefings in Bioinformatics, doi: https://doi.org/10.1093/bib/bbw092. [link]
  6. Gallopin, M., Celeux, G., Jaffrézic, F., Rau, A. (2015) A model selection criterion for model-based clustering of annotated gene expression data. Statistical Applications in Genetics and Molecular Biology, 14(5): 413-428. [link]
  7. 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, 29(2): 205-227. [link]
  8. 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, 31(9): 1420-1427.  [link]
  9. Rau, A., Marot, G. and Jaffrézic, F. (2014) Differential meta-analysis of RNA-seq data from multiple studies. BMC Bioinformatics, 15:91. [link]
  10. 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. [link]
  11. 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. [link]
  12. 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. [link]
  13. 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. [link]
  14. 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. [link]
  15. 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. [link]
  16. 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. [link]

 Statistical applications

  1. Verrier, E., Genet, C., Laloë, D., Jaffrezic, J.,  Rau, A., Esquerre, D., Dechamp, N., Ciobataru, C., Hervet, C., Krieg, F., Quillet, E., Boudinot, P. (2018) Genetic and transcriptomic analyses provide new insights on the early antiviral response to VHSV in resistant and susceptible rainbow trout. BMC Genomics, 19:482. [link]
  2. Maroilley, T., Berri, M., Lemonnier, G., Esquerré, D., Chevaleyre, C., Mélo, S., Meurens, F., Coville, J.L., Leplat, J.J, Rau, A., Bed’hom, B., Vincent-Naulleau, S., Mercat, M.J., Billon, Y., Lepage, P., Rogel-Gaillard, C., and Estellé, J. (2018). Immunome differences between porcine ileal and jejunal Peyer’s patches revealed by global transcriptome sequencing of gut-associated lymphoid tissues. Scientific Reports, 8:9077. [link]
  3. Mondet, F., Rau, A., Klopp, C., Rohmer, M. Severac, D., Le Conte, Y., and Alaux, C. (2018). Transcriptome profiling of the honeybee parasite Varroa destructor provides new biological insights into the mite adult life cycle. BMC Genomics, 19:328. [link]
  4. He, B., Tjhung, K., Bennett, N., Chou, Y., Rau, A., Huang, J., and Derda, R. (2018). Compositional bias in naïve and chemically-modified phage-displayed libraries uncovered by paired-end deep sequencing. Scientific Reports, 8:1214. [link]
  5. Sauvage, C., Rau, A., Aichholz, C., Chadoeuf, J., Sarah, G., Ruiz, M., Santoni, S., Causse, M., David, J., Glémin, S. (2017) Domestication rewired gene expression and nucleotide diversity patterns in tomato. The Plant Journal 91(4):631-645. [link]
  6. 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. [link]
  7. 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. [link]
  8. 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 chapters

  1. Martin-Magniette, M.-L., Maugis-Rabusseau, C. and Rau, A. (2017) Clustering of co-expressed genes. In: Model Choice and Model Aggregation. Ed. F. Bertrand, J.-J. Droesbeke, G. Saporta, C. Thomas-Agnan. [link]

Submitted and pre-prints

  1. Foissac, S., Djebali, S., Munyard, K., Villa-Vialaneix, N., Rau, A., Muret, K., Esquerre, D., Zytnicki, M., Derrien, T., Bardou, P., Blanc, F., Cabau, C., Crisci, E., Dhorne-Pollet, S., Drouet, F., Gonzales, I., Goubil, A., Lacroix-Lamande, S., Laurent, F., Marthey, S., Marti-Marimon, M., Momal-Leisenring, R., Mompart, F., Quere, P., Robelin, D., San Cristobal, M., Tosser-Klopp, G., Vincent-Naulleau, S., Fabre, S., Pinard-Van der Laan, M.-H., Klopp, C., Tixier-Boichard, M., Acloque, H., Lagarrigue, S., Giuffra, E. Livestock genome annotation: transcriptome and chromatin structure profiling in cattle, goat, chicken, and pig. bioRxiv, doi: https://doi.org/10.1101/316091. [link]
  2. Jehl, F., Klopp, C., Brenet, M., Rau, A., Désert, C., Boutin, M., Leroux, S., Muret, K.,  Esquerré, D., Gourichon, D., Burlot, T., Pitel, F., Zerjal, T., Lagarrigue, S. Phenotype and multi-tissue transcriptome response to diet energy change in laying hens. In preparation.