Description du sujet de thèse

Single-cell RNA sequencing (scRNAseq) is revolutionizing biology and medicine. The possibility to assess cellular heterogeneity at a previously inaccessible resolution, has profoundly impacted our understanding of development, of the immune system functioning and of many diseases. While scRNAseq is now mature, the single-cell technological development has shifted to other large-scale quantitative measurements, a.k.a. 'omics', and even spatial positioning. In addition, combined omics measurements profiled from the same single cell are becoming available.

Each single-cell omics presents intrinsic limitations and provides a different and complementary information on the same cell. Single-cell multi-omics integration, i.e. the simultaneous analysis of multiple single-cell omics, is thus expected to compensate for missing or unreliable information in any single omics and to provide tremendous power to untangle the complexity of human cells.
However, single-cell multi-omics integration is challenging. Different single-cell omics vary widely in signal range, in coverage depth and in the number and nature of the measured features. The challenge is thereby to extract biological signals shared across the multiple omics and masked by the wide across-omics variations. In addition, the huge number of profiled cells, billions in the near future, introduces all the computational and statistical challenges typical of “Big Data”. There is thus the imperative need for powerful and robust methodologies able to overcome such challenges and produce new biological knowledge through single-cell omics data integration.

We are looking for a highly motivated PhD student to work at the interface between machine learning and single cell genomics. The PhD student will develop machine learning methods for gene network inference to study cellular heterogeneity and its underlying regulatory mechanisms.

Contexte de travail

The machine learning for integrative genomics laboratory (https://research.pasteur.fr/en/team/machine-learning-for-integrative-genomics/) is an interdisciplinary team composed of 5 researchers in data science, computational biology and bioinformatics. The thesis will be co-supervised by Laura Cantini (team leader machine learning for integrative genomics) and Gabriel Peyré (DR CNRS ENS http://www.gpeyre.com/).

The recruited PhD student will be based with the whole team at the Institut Pasteur, a world-renowned centre of excellence for infectious disease research, ensuring close collaboration with experts in computational biology and laboratory biology. In addition, the affiliation to the PRAIRIE institute, one of the four interdisciplinary research institutes in artificial intelligence set up in the framework of the national strategy for artificial intelligence, allows interaction at the level of AI and machine learning.

Contraintes et risques

None

Informations complémentaires

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