Inference of heterogeneous effects in single-cell genetic perturbation screens

Abstract

AbstractRecent 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 causal effects of perturbations using these technologies. Here we present scCAPE, a tool designed to facilitate causal analysis of heterogeneous perturbation effects at the single-cell level. scCAPE disentangles perturbation effects from the inherent cell-state variations and provides nonparametric inferences of perturbation effects at single-cell resolution, permitting a range of downstream tasks including perturbation effect analysis, genetic interaction analysis, perturbation clustering and prioritizing. We benchmarked scCAPE through simulation studies and real datasets to evaluate its performance in characterizing latent confounding factors and accuracy in estimating heterogeneous perturbation effects. The application of scCAPE identified novel heterogeneous genetic interactions among erythroid differentiation drivers. For example, our analysis pinpointed the role of the synergistic interaction between CBL and CNN1 in the S phase.