Network inference techniques are widely used to identify and characterize complex relationships between genomic, transcriptomic, metabolomics, and proteomic data measured by high-throughput platforms. Network modules—topologically distinct groups of edges and nodes within these networks—that are preserved across datasets can reveal common features of cell-types, tissues, and organisms. Many statistics for assessing the preservation of network module topology have been developed, however, testing their significance requires heuristics [1]. Consequently, these statistics cannot be adjusted for multiple testing, which is important as the number of modules and datasets undergoing module preservation increases with large multi-omic datasets becoming increasingly common and openly available [2].

In a recent study, we demonstrated current statistics for assessing module preservation are systematically biased and produce skewed P–values [3].  We introduced NetRep, a fast, scalable, and statistically rigorous method for assessing module preservation through permutation testing without assuming data are normally distributed. NetRep produces unbiased P-values and can distinguish between true and false positives during multiple hypothesis testing. NetRep is published as an R package on CRAN (https://cran.r-project.org/package=NetRep).

We used NetRep to quantify preservation of gene coexpression modules across murine brain, liver, adipose, and muscle tissues. Complex patterns of multi-tissue preservation were revealed, including a liver-derived housekeeping module that displayed adipose- and muscle-specific association with body weight. Finally, we demonstrate the broad applicability of NetRep by quantifying preservation of bacterial networks in gut microbiota between men and women.