Renée Hoekzema: Multiscale methods for gene selection in single cell transcriptomics data

Single cell transcriptomics is a revolutionary technique in biology that allows for the measurement of gene expression levels in many individual cells simultaneously. Analysis of these large datasets reveals complex variation in expression patterns between cells. Current methods for analysis assume that cell types are discrete. However, in practice there is also continuous variation between cells: subtypes of subtypes, differentiation pathways, responses to environment or treatment, et cetera. The complexity found in modern single cell transcriptomics datasets calls for intricate methods to biologically interpret both discrete clusters as well as continuous variations. We propose topologically-inspired data analysis methods that identify coherent gene expression patterns on multiple scales, considering discrete and continuous patterns on equal footing. As well as finding new biologically meaningful genes, the methodology allows one to visualise and explore the space of gene expression patterns in the dataset.

If time permits I will also talk about ongoing work in a very different direction, namely on mathematical models for co-evolution, for example of parasites and their hosts.