title: From Correlation to Causality: Statistical Approaches to Learning Regulatory Relationships in Large-Scale Biomolecular Investigations.
abstract: 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.
authors.affiliation: Department of Statistics, Purdue University , West Lafayette, Indiana 47907-2066, United States.|College of Science, College of Computer and Information Science, Northeastern University , Boston, Massachusetts 02115, United States.
authors.forename: Robert O
authors.initials: RO
authors.lastname: Ness
authors.affiliation: School of Medicine, Stanford University , Palo Alto, California 94305, United States.
authors.forename: Karen
authors.initials: K
authors.lastname: Sachs
authors.affiliation: College of Science, College of Computer and Information Science, Northeastern University , Boston, Massachusetts 02115, United States.
authors.forename: Olga
authors.initials: O
authors.lastname: Vitek
mesh_terms: D000818:Animals; D015984:Causality; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways; D015233:Models, Statistical; D049490:Systems Biology / Q000379:methods; D018511:Systems Integration
keywords: Bayesian networks; big data; causal inference; causal networks; experimental design