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<p>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 report the development of TEA-GCN (two-tier ensemble aggregation-GCN; <a href="https://github.com/pengkenlim/TEA-GCN">https://github.com/pengkenlim/TEA-GCN</a>), 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 high level of explainability. Furthermore, we show that TEA-GCNs exhibit enhanced conservation across species, making them suitable for multi-species comparative studies.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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

摘要

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 report the development of TEA-GCN (two-tier ensemble aggregation-GCN; https://github.com/pengkenlim/TEA-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 high level of explainability. Furthermore, we show that TEA-GCNs exhibit enhanced conservation across species, making them suitable for multi-species comparative studies.