TestNet: a method for inferring microbial networks with false discovery rate control for clustered and unclustered samples
Abstract
Most existing methods for inferring microbial networks generate only point estimates of Pearson's correlations without assessing their significance, and none accounts for clustering. We introduce TestNet, a novel method that delivers well-calibrated results by controlling the false discovery rate (FDR). TestNet uses a permutation-based procedure to generate valid null replicates that account for compositional effects, excess zeros in microbiome data, and clustering within samples when present. Our results demonstrate that TestNet is the only evaluated method that effectively controls the FDR while maintaining high power across a wide range of scenarios.
Fields of Study & Subjects
Bacillus and Francisella bacterial research
Bacterial biofilms and quorum sensing
SARS-CoV-2 detection and testing
Publication details
- Journal / source
- Genome biology
- Publisher
- Springer Science and Business Media LLC
- Volume
- 27
- Issue
- 1
- Publication date
- 4 June 2026
- Language
- EN