Causal gene regulatory network inference from Perturb-seq via adaptive instrumental variable modeling
Sun, Z.; Kang, H.; Keles, S.
Show 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.