Dahlem Colloquium: Modeling differentiation and stimulation response in single-cell genomics

Accurately modeling single cell 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 graph abstraction as extension of pseudotemporal ordering, and how to take additional information such as RNA velocity into account. I then ask how to generalize predictions to phenomena absent from training data i.e. out-of-sample. For this, I will present scGen, a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. In benchmarks across a broad range of examples, we show that scGen accurately models dose and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell type and species specific response implying that it captures features that distinguish responding from non-responding genes and cells.