Identifying biological perturbation targets through causal differential networks

Identifying variables responsible for changes to a biological system enables applications in drug target discovery and cell engineering. Given a pair of observational and interventional datasets, the goal is to isolate the subset of observed variables that were the targets of the intervention. Directly applying causal discovery algorithms is challenging: the data may contain thousands of variables with as few as tens of samples per intervention, and biological systems do not adhere to classical causality assumptions. We propose a causality-inspired approach to address this practical setting. First, we infer noisy causal graphs from the observational and interventional data. Then, we learn to map the differences between these graphs, along with additional statistical features, to sets of variables that were intervened upon. Both modules are jointly trained in a supervised framework, on simulated and real data that reflect the nature of biological interventions. This approach consistently outperforms baselines for perturbation modeling on seven single-cell transcriptomics datasets. We also demonstrate significant improvements over current causal discovery methods for predicting soft and hard intervention targets across a variety of synthetic data.

Understanding the root causes of change to a biological system can uncover insights behind why drugs work, or how diseases arise. This is challenging through traditional approaches, i.e. try many interventions and see what happens, because the space of options is very large. Our approach follows the inverse direction: we first predict causal relationships that may explain each dataset, and we look for differences in these graphs. We train this model on simulated and real biological data, and the model performs well at recovering these root cases in multiple real, single-cell settings.