Functional enrichment analysis is a computational method used to identify biological functions or pathways that are overrepresented among a list of differentially expressed genes. This analysis can provide insights into the biological processes and molecular mechanisms that are regulated in a particular biological context.
The general workflow for functional enrichment analysis involves the following steps:
- Gene set collection: Gene sets are collections of genes that are associated with a particular biological function or pathway. Gene sets can be obtained from publicly available databases such as Gene Ontology, KEGG, or Reactome, or can be generated de novo based on prior knowledge or experimental data.
- Statistical analysis: The list of differentially expressed genes is compared to each gene set using a statistical test to determine whether the gene set is overrepresented among the differentially expressed genes. Common statistical tests for functional enrichment analysis include the hypergeometric test, the chi-squared test, and the Fisher’s exact test.
- Multiple testing correction: Multiple testing correction methods such as the Benjamini-Hochberg procedure can be used to control the false discovery rate, which is the probability of falsely identifying a gene set as significant.
- Interpretation: The results of the functional enrichment analysis are typically visualized using a graphical representation, such as a bar chart or heatmap, to show the overrepresented biological functions or pathways.
Functional enrichment analysis can be performed using various software tools, such as DAVID, Enrichr, or GSEA. These tools provide pre-built gene sets and user-friendly interfaces for performing functional enrichment analysis. Alternatively, custom gene sets and more flexible analysis options can be implemented using programming languages such as R or Python.
Overall, functional enrichment analysis provides a powerful tool for identifying biological functions or pathways that are overrepresented among a list of differentially expressed genes, and can provide insights into the molecular mechanisms underlying physiological processes and disease states. However, this analysis requires careful consideration of potential confounding factors and experimental biases that could affect the results.