Thanks to the convergence of single-cell biology and massive parallel sequencing, it is now possible to create high-dimensional molecular portraits of cell populations. This technological breakthrough allows for the measurement of gene expression, chromatin states, and genomic variations at single-cell resolution. Single-cell multi-omics has already demonstrated the multiscale basis of disease heterogeneities at the molecular, cellular, individual, and population levels. Moreover, the spatial component of molecular features can also be investigated thanks to spatial transcriptomics techniques. These remarkable advances have resulted in the production of complex high-dimensional data and revolutionized our understanding of the complexity of living tissues, both in normal and pathological states. The full understanding of this ever-increasing complexity of biological data will be possible only by developing analysis strategies able to exploit the ultra-large complexity of the produced data.

New mathematical and computational breakthroughs are required that are inherent to the massive production of heterogeneous, high-resolution, complex, and high-dimensional datasets. These methodological developments will be mandatory to unravel the varying levels of resolution that govern tissue development and organization, and to infer maps of continuous cell states that underlie trajectories towards disease for instance. Artificial Intelligence will also allow us to explore the different levels of resolution that structure biological tissues ranging from molecular and cellular subtypes, to tissue, organisms and cohorts, and to combine them with biological, functional and potentially medical annotations. The field of single-cell data science has emerged, presenting new methodological challenges to fully exploit the potential of single-cell data, such as data normalization, representation and integration, trajectory inference, and cell-cell communication network inference, to name but a few.

The purpose of this workshop will be to foster scientific discussions, collaborations and working sessions between the members of various teams investigating the mathematical aspects of single-cell data analysis, at the confluence between mathematics, statistical learning, and artificial intelligence.