  Latent variable models such as the Variational Auto-Encoder (VAE) have become
a go-to tool for analyzing biological data, especially in the field of
single-cell genomics. One remaining challenge is the interpretability of latent
variables as biological processes that define a cell's identity. Outside of
biological applications, this problem is commonly referred to as learning
disentangled representations. Although several disentanglement-promoting
variants of the VAE were introduced, and applied to single-cell genomics data,
this task has been shown to be infeasible from independent and identically
distributed measurements, without additional structure. Instead, recent methods
propose to leverage non-stationary data, as well as the sparse mechanism shift
assumption in order to learn disentangled representations with a causal
semantic. Here, we extend the application of these methodological advances to
the analysis of single-cell genomics data with genetic or chemical
perturbations. More precisely, we propose a deep generative model of
single-cell gene expression data for which each perturbation is treated as a
stochastic intervention targeting an unknown, but sparse, subset of latent
variables. We benchmark these methods on simulated single-cell data to evaluate
their performance at latent units recovery, causal target identification and
out-of-domain generalization. Finally, we apply those approaches to two
real-world large-scale gene perturbation data sets and find that models that
exploit the sparse mechanism shift hypothesis surpass contemporary methods on a
transfer learning task. We implement our new model and benchmarks using the
scvi-tools library, and release it as open-source software at
https://github.com/Genentech/sVAE.
