KEGG enrichment analysis with clusterProfiler

Overview

Questions

• How can we perform pathway analysis using KEGG?
• What insights can KEGG enrichment provide about differentially expressed genes

Objectives

• Learn how to run KEGG over-representation and GSEA-style analysis in R.
• Understand how to interpret pathway-level results.
• Generate and visualise KEGG pathway figures.

ERROR

Error in `library()`:
! there is no package called 'edgeR'

ERROR

Error in `library()`:
! there is no package called 'goseq'

ERROR

Error in `library()`:
! there is no package called 'fgsea'

ERROR

Error in `library()`:
! there is no package called 'EGSEA'

ERROR

Error in `library()`:
! there is no package called 'clusterProfiler'

ERROR

Error in `library()`:
! there is no package called 'org.Mm.eg.db'

ERROR

Error in `library()`:
! there is no package called 'ggplot2'

ERROR

Error in `library()`:
! there is no package called 'enrichplot'

ERROR

Error in `library()`:
! there is no package called 'pathview'

ERROR

Error in `library()`:
! there is no package called 'edgeR'

ERROR

Error in `library()`:
! there is no package called 'impute'

ERROR

Error in `library()`:
! there is no package called 'preprocessCore'

ERROR

Error in `library()`:
! there is no package called 'RegEnrich'

Introduction

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The KEGG (Kyoto Encyclopedia of Genes and Genomes) database links genes to curated biological pathways, offering a powerful foundation for understanding cellular functions at a systems level and making meaningful biological interpretations. clusterProfiler allows us to access KEGG and apply both ORA (using enrichKEGG function) and GSEA (using gseKEGG function) to extract pathway-level insights from our RNA-seq data.

KEGG analysis

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Before running enrichment, we need to confirm the correct KEGG organism code for mouse (mmu). You can verify by searching:

R

kegg_organism <- "mmu"

search_kegg_organism(kegg_organism, by='kegg_code')

ERROR

Error in `search_kegg_organism()`:
! could not find function "search_kegg_organism"

Over-representation analysis with enrichKEGG

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To run ORA using KEGG database, we need to specify the gene list, KEGG organism code and p-value cut-off. In this example, we take the top 500 genes from the ranked gene list debasal_genelist, specify the organism code mmu (defined as `kegg_organism) and use 0.05 as the p-value cut-off.

We can use head() function to briefly inspect the results of enrichKEGG.

R

kk <- enrichKEGG(gene         = names(debasal_genelist)[1:500],
                 organism     = kegg_organism,
                 pvalueCutoff = 0.05)

ERROR

Error in `enrichKEGG()`:
! could not find function "enrichKEGG"

R

head(kk)

ERROR

Error:
! object 'kk' not found

GSEA-style KEGG enrichment with gseKEGG

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Similar to previous enrichment analysis with GO database, we can also perform a GSEA-style enrichment using the KEGG database. To do so, we use the gseKEGG and specify the entire ranked gene list (debasal_genelist) rather than an arbitrary cutoff. In this example, we test KEGG pathways between 3 and 800 genes using 10,000 permutations and NCBI Gene IDs. Results are filtered using a p-value cut-off of 0.05.

R

kk2 <- gseKEGG(geneList     = debasal_genelist,
               organism     = kegg_organism,
               nPerm        = 10000,
               minGSSize    = 3,
               maxGSSize    = 800,
               pvalueCutoff = 0.05,
               pAdjustMethod = "none",
               keyType       = "ncbi-geneid")

ERROR

Error in `gseKEGG()`:
! could not find function "gseKEGG"

Visualising enriched pathways

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Dotplot

Before we look at individual pathways in detail, we can visualise the overall enrichment results using dotplot().
This dotplot summarises which KEGG pathways are enriched, how many genes contribute to each pathway, and how significant each one is.

R

dotplot(kk2, showCategory = 10, title = "Enriched Pathways" , split=".sign") + facet_grid(.~.sign)

ERROR

Error in `dotplot()`:
! could not find function "dotplot"

Similarity-based network plots

Next, we can explore how the enriched pathways relate to one another.
The enrichment map groups pathways that share many genes, helping us see broader biological themes rather than isolated pathways. In this case, pairwise_termsim() function calculates the similarity between enriched KEGG pathways and produces a similarity matrix that quantifies their relationship. The emapplot()generates an enrichment map using the similarity matrix produced, visualising the enriched pathways as a network with nodes representing pathways and edges reflecting their similarity.

R

kk3 <- pairwise_termsim(kk2)

ERROR

Error in `pairwise_termsim()`:
! could not find function "pairwise_termsim"

R

emapplot(kk3)

ERROR

Error in `emapplot()`:
! could not find function "emapplot"

We can also use cnetplot() to understand which genes drive these enriched pathways. This plot links genes to pathways they belong to and highlights genes that appear in multiple pathways.

R

cnetplot(kk3, categorySize="pvalue")

ERROR

Error in `cnetplot()`:
! could not find function "cnetplot"

Ridge plot

We can also inspect the distribution of enrichment scores across pathways with ridgeplot(). This shows how strongly and broadly each pathway is enriched across the ranked gene list using overlapping density curves. 

R

ridgeplot(kk3) + labs(x = "enrichment distribution")

ERROR

Error in `ridgeplot()`:
! could not find function "ridgeplot"

R

head(kk3)

ERROR

Error:
! object 'kk3' not found

You can see the top pathways, you can get the top pathway ID with the ID column.

R

# There must be a function that gets the results -> not ideal code
kk3@result$ID[1]

ERROR

Error:
! object 'kk3' not found

KEGG Pathway Diagram

Finally, we can visualise gene expression changes directly onto a KEGG pathway diagram.
pathview highlights which components of the pathway are up- or down-regulated in your enrichment analysis.

R

# Produce the native KEGG plot (PNG)
mmu_pathway <- pathview(gene.data=debasal_genelist, pathway.id=kk3@result$ID[1], species = kegg_organism)

These will produce these files in your working directory:

mmu05171.xml mmu05171.pathview.png mmu05171.png

Figure of output produced

Key Points

• KEGG pathway analysis helps link DEGs to functional biological pathways.

• Both ORA (enrichKEGG) and GSEA-style (gseKEGG) methods provide complementary insights.

• pathview enables visual interpretation of pathway-level expression changes.