Fig. 1: An overview of causal gene–gene network inference in mixed observational and perturbational single-cell data.
From: A large-scale benchmark for network inference from single-cell perturbation data
The causal generative process in its unperturbed form is observed in the observational data (left; 10,000+ datapoints in CausalBench) while data under genetic interventions (e.g. CRISPR knockouts) are observed in the interventional data (right; 200,000+ datapoints in CausalBench). Either observational or interventional plus observational data that were sampled from the true causal generative process (bottom distributions) can be used by network inference algorithms (bottom right) to infer a reconstructed causal graph (top right) that should as closely as possible recapitulate the original underlying functional gene-gene interactions.
