Conclusion

Estimated time: 12 minutes

Overview

Questions

• What have we learned about functional enrichment and pathway analysis?
• How do different methods complement one another when interpreting RNA-seq results?

Objectives

• Summarise the key concepts introduced across the lesson series.
• Understand how different gene set and network tools fit together in a typical analysis workflow.
• Recognise when and why to choose each enrichment method.

Conclusion

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In this tutorial, we have explored several complementary approaches for interpreting RNA-seq results beyond differential expression alone. Through using these various R packages, we are able to get insights biological processes and pathways involved in the differential expression of genes observed.

Specifcally, we worked through:

• Over-representation analysis (ORA) with clusterProfiler
  
• Gene set enrichment analysis (GSEA) using fgsea
  
• Regulatory network analysis with RegEnrich
  
• Protein–protein interaction networks via STRINGdb

Although each tool uses different assumptions and statistical frameworks, they all aim to answer a similar biological question:

Which biological processes, pathways, or regulators help explain the gene expression changes we observe?

By applying multiple methods, you can cross-validate findings and gain a more complete picture of the molecular biology underlying your condition of interest.

You should now feel comfortable:

• preparing gene lists or ranked gene sets
  
• running several types of enrichment analyses
  
• visualising pathway-level patterns
  
• integrating results from complementary tools
  
• exploring interaction networks and regulatory drivers

These approaches form a core part of transcriptomic interpretation and are widely used in modern functional genomics.

Key Points

• Enrichment methods help translate gene-level changes into biological meaning.
  
• Different tools (ORA, GSEA, network-based methods) answer different but complementary questions.
  
• Combining methods provides stronger and more interpretable biological insights.
  
• Functional enrichment is an essential component of any RNA-seq analysis workflow.