Skip to content

Gene Ontology Enrichment Analysis

Analyzing GO Enrichment from DEGs

Gene Ontology (GO) enrichment analysis is used to identify biological processes, cellular components, and molecular functions that are significantly over-represented (or under-represented) in a set of genes compared to a background list. This is particularly valuable when analyzing differentially expressed genes (DEGs) identified from RNA-seq or microarray experiments.

Individual Gene Analysis (IGA)

  • Concept

This approach tests each GO term individually for enrichment within the DEG list.

  • Methods:

    • Hypergeometric test: Calculates the probability of observing the number of DEGs in a specific GO term by chance.
    • Fisher's exact test: Similar to the hypergeometric test but suitable for smaller datasets.

  • Limitations:

    • Ignores the hierarchical structure of GO, potentially missing related terms.
    • Susceptible to multiple testing issues, requiring correction methods like Bonferroni adjustment.

  • Advanced Considerations:

    • Multiple Testing Correction: As mentioned, IGA is susceptible to multiple testing issues. Here are some commonly used correction methods:
      • Bonferroni adjustment: A conservative approach that controls the family-wise error rate (FWER) but can be overly stringent.
      • Benjamini-Hochberg (BH) procedure: Controls the false discovery rate (FDR) and is less conservative than Bonferroni.
      • False discovery rate (q-value): Provides a measure of significance adjusted for multiple testing.
    • Gene Ontology Consortium (GOC) recommendations: The GOC recommends using a combination of statistical significance (p-value) and fold change thresholds to identify relevant enriched terms, acknowledging the limitations of p-values alone.

Gene Set Analysis (GSA)

  • Concept:

Considers the entire set of DEGs and their relationships within the GO hierarchy.

  • Methods:

    • Pathway analysis tools: Tools like Enrichr, clusterProfiler, and GSEA analyze pre-defined gene sets like KEGG pathways and analyze enrichment within DEGs.
    • GO-based GSA methods:
      • Rank-based approaches: Assign a rank to each gene based on its differential expression and analyze enrichment within ranked gene sets. (e.g., GSEA)
      • Permutation-based approaches: Randomly shuffle gene labels and recalculate enrichment scores to assess statistical significance. (e.g., fgsea)
      • Tools like GOseq, fgsea, and piano utilize various statistical models to account for the hierarchical structure of GO and identify enriched functional categories.

  • Advantages of using GSA:

    • Incorporates information about gene relationships within the GO hierarchy, leading to more biologically relevant insights.
    • Reduces the burden of multiple testing compared to individual GO term analysis.

Advanced Methods for Deeper Exploration

  • Cluster enrichment analysis: Tools like CeaGO group related GO terms based on semantic similarity and analyze enrichment within these clusters. This approach can reveal broader functional themes beyond individual terms.
  • Network analysis: Integrating protein-protein interaction data with GO annotations allows identifying functionally connected subnetworks enriched in DEGs. This provides a network-based understanding of the underlying biological processes.

Choosing the right method

The choice of method depends on factors like:

  • Size of the DEG list: For smaller lists, IGA might be sufficient, while larger lists benefit from GSA approaches.
  • Research question: If interested in specific GO terms, IGA might be suitable. For broader functional insights, GSA is preferred.

Additional considerations

  • Over-detection bias standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly-expressed transcripts. The goseq tool provides methods for performing GO analysis of RNA-seq data, taking length bias into account. The methods and software used by goseq are equally applicable to other category based tests of RNA-seq data, such as KEGG pathway analysis.
  • Background gene list: Choosing a relevant background list representing the genes not differentially expressed is crucial for accurate enrichment analysis.
  • Multiple testing correction: Apply appropriate correction methods to account for testing multiple GO terms simultaneously.
  • Visualization: Utilize graphical representations like bar charts or heatmaps to visualize enriched GO terms and their significance levels.