False Discovery Rates in Biological Networks
arXiv:1907.03808
Abstract
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 show that this new estimator guarantees accurate control of the false discovery rate in theory, simulations, and biological applications, and we provide easy-to-use R code.
References in corpus (7)
- Sparse and compositionally robust inference of microbial ecological networks
- The huge Package for High-dimensional Undirected Graph Estimation in R
- On the Benjamini--Hochberg method
- Multiple Testing and Error Control in Gaussian Graphical Model Selection
- A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
- The knockoff filter for FDR control in group-sparse and multitask regression
- Integrating Additional Knowledge Into Estimation of Graphical Models