False Discovery Rates in Biological Networks
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:163-171, 2021.
The increasing availability of data has generated unprecedented prospects for network analyses in many biological fields, such as neuroscience (e.g., brain networks), genomics (e.g., gene-gene interaction networks), and ecology (e.g., species interaction networks). A powerful statistical framework for estimating such networks is Gaussian graphical models, but standard estimators for the corresponding graphs are prone to large numbers of false discoveries. In this paper, we introduce a novel graph estimator based on knockoffs that imitate the partial correlation structures of unconnected nodes. We then show that this new estimator provides accurate control of the false discovery rate and yet large power.