Massively parallel sequencing applied to single cells allows us to investigate new questions that were out of reach for classical bulk genomics. Cell-to-cell variability is central in gene regulation or cell differentiation, as it provides information on the underlying molecular networks. Consequently, single cell expression profiling has the promise of revolutionizing our understanding of genomes regulation.

Nevertheless, the specific characteristics of single cell data as well as their dimensionality calls for new mathematical models and computational tools. The goal of this project is to develop new methodologies to investigate cell identity and the dynamics of cell differentiation, by integrating single cell expression and epigenomic data. For that purpose, our consortium gathers unique combined expertises in statistics, machine learning, optimal transport and systems biology, and an extended network of collaborators on single-cell (medical) genomics in France and abroad.

Recent Publications

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Meetings

Projects

PhD thesis of Anthony Ozier-Lafontaine

High-dimensional statistics for single-cell RNA sequencing data

PhD thesis of Elias Ventre

Modelling single-cell differentiation with PDMP

PhD thesis of Claire Gayral

Single-cell data integration

Consortium

Team leaders

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Team leader, AgroParisTech/INRA

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Team leader, LBMC

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Team leader, LMJL, Nantes

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Coordinator, Team leader of LBBE

Students

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PhD Student, LBBE/AgroParisTech

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PhD Student, LMJL, Nantes

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PhD Student, INRIA/ENS Lyon