Inference of heterogeneous effects in single-cell genetic perturbation screens

Recent single-cell CRISPR screening experiments have combined the advances of genetic editing and single-cell technologies, leading to transcriptome-scale readouts of responses to perturbations at single-cell resolution. An outstanding question is how to efficiently identify heterogeneous effects of perturbations using these technologies. Here we present CausalPerturb, which leverages AI tools and causal analysis to dissect the heterogeneous landscape of perturbation effects. CausalPerturb disentangles transcriptome changes introduced by perturbations from those reflecting inherent cell-state variations. It provides nonparametric inferences of perturbation effects, enabling a range of downstream tasks including genetic interaction analysis, perturbation clustering and prioritization. We evaluated CausalPerturb through simulation studies and real datasets, and demonstrated its competence in characterizing latent confounding factors and discerning heterogeneous perturbation effects. The application of CausalPerturb unraveled novel genetic interactions between erythroid differentiation drivers. In particular, it pinpointed the role of the synergistic interaction between CBL and CNN1 in the S phase.