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Biological Functional Class Enrichment Analysis with R, an Annotated Tutorial for Bench Scientists.

Kejin Hu1

  • 1Department of Microbiology, Immunology and Genetics, College of Biomedical and Translational Sciences, University of North Texas Health Science Center, Fort Worth, TX 76107, USA.

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|February 26, 2026
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Summary
This summary is machine-generated.

This tutorial provides R scripts for biological functional class enrichment analysis (FunCEA) to identify gene groups linked to biological conditions. It details methods like over-representation analysis and functional class scoring using gene ontology, KEGG, and reactome databases.

Keywords:
Kyoto Encyclopedia of Genes and Genomes (KEGG)category network plot (cnetplot)clusterProfilerenrichment visualizationfunctional class enrichment analysis (FunCEA)functional class scoring (FCS)gene ontology (GO)gene set enrichment analysis (GSEA)over-representation analysis (ORA)pathway enrichment analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing generates gene lists, necessitating methods to identify functionally relevant gene groups.
  • Biological functional class enrichment analysis (FunCEA) addresses the need to link gene expression changes to specific biological or biomedical conditions.

Purpose of the Study:

  • To provide bench scientists with accessible R protocols for FunCEA, including data processing and visualization.
  • To detail two popular FunCEA methods: over-representation analysis (ORA) and functional class scoring (FCS).

Main Methods:

  • Utilizing R, a robust platform for statistical computing and graphics.
  • Implementing FunCEA using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and reactome knowledge databases.
  • Providing detailed R scripts for enrichment analysis, data processing, and visualization.

Main Results:

  • Demonstration of FunCEA using ORA and FCS methods within the R environment.
  • Inclusion of R code for diverse visualizations: dot plots, term-gene network plots, enrichment maps, ridge plots, and GSEA plots.
  • Highlighting the utility of the `clusterProfiler` package for accessing the KEGG database.

Conclusions:

  • This R tutorial offers a comprehensive guide for bench scientists to perform FunCEA without commercial software.
  • The provided scripts and explanations facilitate the interpretation of gene enrichment results for differential biological conditions.
  • The study emphasizes the power of R and specific packages for reproducible and accessible functional enrichment analysis in biomedical research.