Causal gene regulatory network inference from Perturb-seq via adaptive instrumental variable modeling

Abstract

Inferring causal gene regulatory networks (GRNs) from observational single-cell data is challenging due to confounding. While Perturb-seq provides causal leverage, existing methods are often biased by heterogeneous CRISPRi knockdown efficiencies and restrictive assumptions like acyclicity. We present ADAPRE, a framework that treats CRISPR interventions as instrumental variables within a Poisson-lognormal model. By adaptively accounting for variable perturbation strength, ADAPRE recovers potentially cyclic structures and outperforms existing methods. Applied to a genome-wide K562 Perturb-seq dataset, it reconstructs networks enriched for known biological interactions and identifies coherent, leukemia-associated subnetworks, establishing a scalable approach for causal GRN inference.