Causality-oriented regulatory inference and rational transcriptomic reprogramming design with CASCADE

Abstract

Systematically elucidating the causal structure of gene expression regulation is the foundation for identifying key regulators of cellular function and designing rational interventions thereof. While advances in high-content perturbation screens enables experimental probing of causal regulatory effects, the massive genomic scale, measurement noise and confounding factors pose significant challenges for existing causal discovery and inference algorithms. Here, we present CASCADE, an end-to-end framework that integrates de novo causal discovery with prior regulatory knowledge for genome-wide causal discovery and inference. In addition to its superior performance on causal regulatory structure discovery and counterfactual inference for unseen perturbation effects, CASCADE enables, for the first time, causality-oriented rational intervention design for transcriptomic programming towards various targeted cells within a unified framework.