# A scalable permutation approach reveals replication and preservation patterns of gene coexpression modules

## TL;DR

This paper introduces a new method to assess how gene networks are preserved across different tissues and species, revealing patterns of shared function.

## Contribution

The paper introduces NetRep, a rapid and unbiased permutation-based approach for evaluating module preservation in gene coexpression networks.

## Key findings

- 52% of gene modules showed unambiguous preservation in one or more tissues.
- 25% of modules were preserved across all four tissues (brain, liver, adipose, muscle).
- A liver-derived gene module was associated with body weight in adipose and muscle tissues.

## Abstract

Gene coexpression network modules provide a framework for identifying shared biological functions. Analysis of topological preservation of modules across datasets is important for assessing reproducibility, and can reveal common function between tissues, cell types, and species. Although module preservation statistics have been developed, heuristics have been required for significance testing. However, the scale of current and future analyses requires accurate and unbiased p-values, particularly to address the challenge of multiple testing. Here, we developed a rapid and efficient approach (NetRep) for assessing module preservation and show that module preservation statistics are typically non-normal, necessitating a permutation approach. Quantification of module preservation across brain, liver, adipose, and muscle tissues in a BxH mouse cross revealed complex patterns of multi-tissue preservation with 52% of modules showing unambiguous preservation in one or more tissues and 25% showing preservation in all four tissues. Phenotype association analysis uncovered a liver-derived gene module which harboured housekeeping genes and which also displayed adipose and muscle tissue specific association with body weight. Taken together, our study presents a rapid unbiased approach for testing preservation of gene network topology, thus enabling rigorous assessment of potentially conserved function and phenotype association analysis.