Work

Constructing gene co-functional and co-regulatory networks from public transcriptomes using condition-specific ensemble co-expression

Year: 2026

Type: article

Abstract: Gene co-expression networks (GCNs) can reveal useful gene co-functional and co-regulatory relationships. However, current GCN construction methodologies are sensitive to batch effects and sample composition, limiting their performance in generating GCNs from public RNA-seq samples abundant for many species. Here, we detail the development of TEA-GCN (Two-Tier Ensemble Aggregation-GCN), a GCN construction method that leverages unsupervised transcriptomic dataset partitioning and multi-metric co-expression scoring to derive ensemble gene co-expression. Benchmarking over 450,000 public RNA-seq samples across 12 species, TEA-GCN outperforms the state-of-the-art in predicting gene functions and inferring gene regulatory networks. Through the use of natural language processing, we also show that the biologically-relevant dataset partitions with high co-expression can identify tissue-/condition-specific co-expression in TEA-GCN, providing an unprecedented level of explainability. Furthermore, we show that TEA-GCNs exhibit enhanced conservation across species, making them suitable for multi-species comparative studies. TEA-GCN is available at https://github.com/pengkenlim/TEA-GCN .

Source: Nature Communications

DOI: 10.1038/s41467-026-72380-1

Authors: Peng Ken Lim, Ruoxi Wang, Shan Chun Lim, Jenet Princy Antony Velankanni, Marek Mutwil

Institutions: Nanyang Technological University, University of Copenhagen

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $6,990