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GeneCOCOA: Detecting context-specific functions of individual genes using co-expression data.

Simonida Zehr1,2, Sebastian Wolf3, Thomas Oellerich3

  • 1Goethe University Frankfurt, Institute for Cardiovascular Physiology, Frankfurt am Main, Germany.

Plos Computational Biology
|March 31, 2025
PubMed
Summary

GeneCOCOA analyzes gene expression by focusing on a single gene of interest, using co-expression to identify related biological pathways and functions. This method offers a novel approach for understanding gene roles and regulation, outperforming existing tools.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Traditional gene set enrichment analysis (GSEA) methods focus on predefined gene sets, limiting analysis for single genes lacking prior functional characterization.
  • Existing GSEA approaches do not effectively address the need for experiment-specific analyses centered around a gene of interest (GOI).

Purpose of the Study:

  • To develop a novel method, GeneCOCOA, for gene set enrichment analysis focused on a user-supplied gene of interest (GOI).
  • To leverage context-specific gene co-expression and curated functional gene sets to infer gene function and regulatory mechanisms.
  • To provide a gene-focused, experiment-specific approach for biological insight discovery.

Main Methods:

  • GeneCOCOA utilizes linear regression to derive co-expression between the GOI and subsets of genes from functional groups.
  • Root-mean-square error (RMSE) values are compared against background values from randomly selected genes to generate p-values.
  • A differential GeneCOCOA mode is included for experiment-specific comparisons.

Main Results:

  • GeneCOCOA statistically ranks functional gene sets based on their co-expression with the GOI, specific to the experimental context.
  • The method provides biological insights into gene function and potential regulatory mechanisms controlling the GOI.
  • GeneCOCOA demonstrates superior performance in recalling known gene-disease associations compared to similar methods.

Conclusions:

  • GeneCOCOA offers a powerful, gene-centric approach to gene set enrichment analysis, enhancing biological insight discovery.
  • The R package implementation facilitates ease-of-use for researchers in bioinformatics and computational biology.
  • This method advances the analysis of gene expression data by focusing on individual genes and their functional relationships.