Quentin
PhD student at the MICS Laboratory (Paris-Saclay University) and the Gustave Roussy Institute. I'm developing deep learning tools on multi-omics data for precision medicine in oncology. I have a particular focus on spatial omics data with single-cell resolution.
Sessions
Spatial transcriptomics is revolutionizing the field of molecular biology by providing high-resolution insights into gene expression within the spatial context of tissues. This technology is crucial for annotating spatial domains (or niches), which allows researchers to understand the spatial organization of gene expression and its implications for tissue function and disease progression. Although many studies have already explored this area, current models lack versatility as they cannot run on different gene panels and must be retrained for each new task or sample.
We introduce Novae, a graph-based foundation model for spatial omics designed to overcome these limitations. Novae is a self-supervised model focused on extracting a representation of a cell within its niche context. Trained on a dataset of 21 million cells across 12 different tissues, Novae performs zero-shot inference on all gene panels, automatically correcting batch effects and creating a nested hierarchy of niches. It also supports various downstream tasks, including whole-genome expression inference, spatially variable gene analysis, and niche trajectory analysis. Overall, Novae offers a powerful and versatile tool for advancing our understanding of spatial transcriptomics and its applications in biomedical research.