Mechanism of KEGG Pathway Enrichment Analysis

Pathway enrichment analysis is widely utilized in biological research to understand the functional roles of genes or proteins in various biological processes. KEGG (Kyoto Encyclopedia of Genes and Genomes) is one of the most commonly used bioinformatics resources for pathway enrichment analysis. KEGG pathway enrichment analysis reveals the enrichment of specific biological pathways in a gene set by comparing gene expression differences under different experimental conditions.

 

The KEGG database integrates extensive information from genomics, chemical molecular biology, and systems biology, presenting a systematic map of interactions and transformations among molecules inside and outside the cell. This database maps genes and proteins to known molecular pathways, providing researchers with a comprehensive biological network map. This mapping relationship forms the basis for pathway enrichment analysis, enabling researchers to identify pathways significantly enriched under specific biological conditions.

 

Mechanism of KEGG Pathway Enrichment Analysis

1. Gene Set Selection and Data Preprocessing

KEGG pathway enrichment analysis begins with a gene set of interest, often obtained through high-throughput techniques such as RNA-Seq or microarray analysis. Initially, these genes are selected and undergo data preprocessing to ensure data quality for analysis. This preprocessing may involve normalization of gene expression levels, noise reduction, and differential expression analysis to ensure that the selected gene set accurately reflects biological changes under the experimental conditions.

 

2. Mapping Genes to KEGG Pathways

After data preprocessing, the target gene set is mapped to the KEGG pathway database. This mapping process matches gene IDs with known gene information in KEGG, associating the target genes with specific biological pathways. Through this mapping, researchers can identify which genes in the gene set are associated with KEGG pathways, providing data support for further enrichment analysis.

 

3. Enrichment Statistical Testing

A key step in enrichment analysis is using statistical methods to determine which pathways are significantly enriched in the target gene set. Typically, enrichment analysis relies on statistical tests such as the hypergeometric distribution or Fisher's exact test to evaluate whether the observed number of genes in a pathway is significantly higher than expected. If a pathway shows a level of enrichment significantly higher than would be expected randomly, it is inferred that this pathway might have an important function in the biological condition.

 

4. Multiple Hypothesis Testing Correction

To control for the false discovery rate (FDR) that arises from multiple testing, multiple hypothesis testing correction is applied in KEGG enrichment analysis. Common methods include Bonferroni correction and Benjamini-Hochberg correction. The former reduces the significance threshold to minimize significant results, while the latter adjusts the significance level to control FDR. The pathways deemed significant after correction are considered biologically relevant candidate pathways.

 

5. Pathway Functional Interpretation and Biological Inference

Once significant pathways are identified, researchers interpret the results functionally by combining experimental data and biological knowledge. By annotating the functions of these significant pathways, researchers can hypothesize the roles of these pathways in the biological system. In complex disease research, such as cancer and metabolic disorders, KEGG enrichment analysis helps identify key molecular networks and regulatory mechanisms.

 

KEGG pathway enrichment analysis provides a robust tool for interpreting the function of gene or protein sets. Its core mechanism includes gene mapping, statistical enrichment, hypothesis correction, and functional interpretation. Together, these steps aid researchers in uncovering the biological significance within genomics and proteomics data. As bioinformatics advances, the applications of KEGG pathway enrichment analysis will continue to expand in functional genomics, disease research, and drug development.