Leading research that is inherently multidisciplinary and diverse, at the crossroads between statistics and genomics, I have developed several statistical methodologies and lead several studies in human genetics. Throughout my collaborations and experience, I have developed expertise in the genetics of the metabolic syndrome with a long term ambition to take part into the rising era of personalized medicine.


PhD Thesis

“Exploratory Analysis of transcriptomic data: from their visualisation to the integration of external information”


We propose new methodologies of exploratory statistics which are dedicated to the analysis of transcriptomic data (DNA microarray data). Transcriptomic data provide an image of the transcriptome which itself is the result of phenomena of activation or inhibition of gene expression.

However, the image of the transcriptome is noisy. That is why, firstly we focus on the issue of transcriptomic data denoising, in a visualisation framework. To do so, we propose a regularised version of principal component analysis. This regularised version allows to better estimate and visualise the underlying signal of noisy data.

In addition, we can wonder if the knowledge of only the transcriptome is enough to understand the complexity of relationships between genes. That is why we propose to integrate other sources of information about genes, and in an active way, in the analysis of transcriptomic data. Two major mechanisms seem to be involved in the regulation of gene expression, regulatory proteins (for instance transcription factors) and regulatory networks on the one hand, chromosomal localisation and genome architecture on the other hand.

Firstly, we focus on the regulation of gene expression by regulatory proteins; we propose a gene clustering algorithm based on the integration of functional knowledge about genes, which is provided by Gene Ontology annotations. This algorithm provides clusters constituted by genes which have both similar expression profiles and similar functional annotations. The clusters thus constituted are then better candidates for interpretation.

Secondly, we propose to link the study of transcriptomic data to chromosomal localisation in a methodology developed in collaboration with geneticists.


transcriptomic data, multidimensional analysis, visualisation, regularisation,
integration of biological knowledge, Gene Ontology, chromosomal localisation

Examination committee

  • Hervé Abdi (University of Texas at Dallas, USA), Rapporteur
  • Philippe Besse (Institut de Mathématiques de Toulouse, France) ,Rapporteur
  • Jean Mosser (CNRS Université de Rennes 1, France), Président
  • Sandrine Lagarrigue (INRA/Agrocampus Ouest, Rennes, France), Examinatrice
  • Jérôme Pagès (Agrocampus Ouest, Rennes, France), Directeur de thèse
  • Sébastien Lê (Agrocampus Ouest, Rennes, France), Directeur de thèse

Manuscript (in french) and Slides (in french)


PhD defense video (in french)

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