Speaker: David Fischer, Theis Lab, ICB, Helmholtz Zentrum, Munich, Germany
Cell Biology revolves around the idea that cells are core functional units of organisms: Models for cellular function explain many disease and treatment effects, for example. Hence, there has been a long-standing, significant interest in improving the understanding of cellular behaviour. Recent experimental advances enable increasingly detailed molecular profiling of cells: Examples include methods such as single-cell RNA sequencing, which measure quantities of particular molecular species in each cell. The resulting output can be summarised as a matrix (cells x molecular features) with up to millions of cells and 10,000s of features. Models for variance in these data sets are actively developed in Computational Biology; many concepts from unsupervised learning are used in this context. While these unsupervised techniques yield useful descriptions of the molecular state space, they usually fail to yield a complete picture of molecular mechanisms of causality underlying this variance. Here, I discuss approaches centred on supervision tasks in single-cell Immunology which improve interpretability of these models and outline ways of generalising this approach to other settings in single-cell Biology. Lastly, I discuss challenges and opportunities of representation learning on whole tissues, bridging the gap from cell-centric models to tissue function models, such as for tumor behaviour.