Comparison of Interventional Causal Structure Learning Algorithms for Gene Regulatory Network Inference

Abstract

BackgroundCausal structure learning offers a promising approach to studying gene regulation in cells, aiming to provide deeper mechanistic insights than purely association-based methods. Theoretical groundwork indicating that interventions improve identifiability of causal structure motivates the use of causal structure learning methods in scenarios with interventional information. ResultsThis benchmark investigates the ability of existing causal structure learning algorithms to leverage the information revealed by targeted interventions to infer gene regulatory networks (GRNs). In this study both synthetic and experimental single-cell CRISPR perturbation data is leveraged, and a suite of causal structure learning algorithms, is evaluated on metrics tailored to synthetic ground truth and real biological data respectively. On synthetic data, accurate recovery of GRNs is achieved under favourable conditions: strong interventions, large sample sizes, and low measurement noise. However, on real data, performance remains unreliable, limited by technical and biological noise, as well as algorithmic scalability. This highlights a current gap between theoretical potential and practical application of causal structure learning for GRNs. ConclusionsThe benchmark provides insight into algorithm strengths and limitations and offers groundwork for further methodological development. We also provide an accessible software package to leverage modern causal structure learning on custom datasets and thereby foster future exploration of the potential of causal structure learning.