KEGG Pathway Enrichment Analysis Guide

Overview

KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis identifies biological pathways that are statistically over-represented among differentially expressed genes. This guide covers the two main enrichment approaches (ORA and GSEA), critical workflow decisions such as splitting genes by directionality, tool selection between R clusterProfiler and Python gseapy, and strategies for handling the notoriously unreliable KEGG REST API. It addresses recurring failure modes that produce incorrect pathway counts or stalled analyses.

The three most common errors in KEGG pathway analysis are: (1) combining up-regulated and down-regulated genes into a single enrichment run, which masks true pathway signals; (2) analysis failures caused by KEGG REST API timeouts with no fallback strategy; and (3) delaying result reporting while attempting cosmetic pathway name lookups that may never complete. This guide provides concrete solutions for each.

Key Concepts

ORA vs GSEA

Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) are the two primary methods for pathway enrichment, and they differ in both input and statistical approach.

ORA takes a pre-filtered gene list (e.g., genes with padj < 0.05 and |log2FC| > 1.5) and tests whether KEGG pathway members are over-represented in that list relative to a background universe. ORA uses a hypergeometric test (Fisher's exact test). It is straightforward but discards magnitude information and depends heavily on the significance cutoff chosen.

GSEA takes a ranked list of all genes (typically ranked by log2 fold change or a signed significance statistic) without any cutoff. It computes a running enrichment score by walking down the ranked list and identifies pathways whose members cluster toward the top or bottom of the ranking. GSEA captures subtle coordinated changes that ORA may miss.

In practice, ORA via enrichKEGG() (clusterProfiler) or gp.enrichr() (gseapy) is the more common starting point. GSEA via gseKEGG() or gp.prerank() is preferred when you want to avoid arbitrary cutoffs or when effect sizes are small.