From Correlation to Causality: Statistical Approaches to Learning Regulatory Relationships in Large-Scale Biomolecular Investigations

Causal inference, the task of uncovering regulatory relationships between components of biomolecular pathways and networks, is a primary goal of many high-throughput investigations. Statistical associations between observed protein concentrations can suggest an enticing number of hypotheses regarding the underlying causal interactions, but when do such associations reflect the underlying causal biomolecular mechanisms? The goal of this perspective is to provide suggestions for causal inference in large-scale experiments, which utilize high-throughput technologies such as mass-spectrometry-based proteomics. We describe in nontechnical terms the pitfalls of inference in large data sets and suggest methods to overcome these pitfalls and reliably find regulatory associations.