Dresden International Graduate School for Biomedicine and Bioengineering

Modeling cellular state changes e.g. during differentiation or in response to perturbations is a central goal of computational biology. Single-cell technologies now give us easy and large-scale access to state observations on the transcriptomic and more recently also epigenomic level. In particular, they allow resolving potential heterogeneities due to asynchronicity of differentiating or responding cells, and profiles across multiple conditions such as time points and replicates are being generated. In this talk I will quickly review how to estimate lineage formation using pseudotemporal ordering, and how to add additional information such as RNA velocity and latent space learning to form models of cell response.