KEGG Pathway Enrichment Analysis for RNA-seq: A Practical Guide to Interpreting Pathway Results

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-20

Key Takeaways

• KEGG pathway enrichment analysis identifies biological pathways overrepresented by differentially expressed genes from RNA-seq data, answering the question: "Which known molecular pathways contain more of my changed genes than expected by chance?" This analysis requires a list of differentially expressed genes and a background list of all tested genes, with accurate gene identifier matching (e.g., Entrez Gene IDs) to KEGG crucial for valid results.
• The analysis relies on KEGG's structured database of manually curated pathway maps, where pathway size and gene membership influence statistical power; smaller pathways with a higher proportion of differentially expressed genes may yield statistically significant but biologically narrow findings. Over-representation analysis uses a thresholded gene list, while gene set enrichment analysis utilizes the full ranked list of genes, offering greater sensitivity for subtle pathway changes.
• Practical implementation typically involves R and Bioconductor packages like clusterProfiler for over-representation analysis, requiring careful selection of the correct organism code (e.g., hsa for human) and conversion of gene identifiers. Visualization tools such as pathview are essential for mapping differentially expressed genes onto KEGG pathway diagrams, aiding in the interpretation of directionality and specific gene involvement.
• Common failure patterns include gene identifier mismatches, incorrect background gene lists (using the entire genome instead of detected genes), and ignoring the direction of gene expression change; running separate analyses for up- and down-regulated genes can clarify biological context. Interpreting results necessitates considering both adjusted p-values for multiple testing correction (e.g., Benjamini-Hochberg) and effect sizes (gene ratio, number of genes) to assess biological significance beyond statistical significance.
• KEGG enrichment analysis provides complementary information to Gene Ontology (GO) enrichment, offering insights into molecular interaction networks rather than individual gene functions, and is increasingly integrated into multi-omics studies (e.g., with ATAC-seq) and single-cell RNA-seq analyses to identify coordinated regulatory mechanisms. Reproducibility is paramount, requiring meticulous documentation of software versions, database releases, and analytical parameters.

Direct Answer and Scope

KEGG pathway enrichment analysis is a computational method that tests whether differentially expressed genes from an RNA-seq experiment are overrepresented in specific biological pathways cataloged in the Kyoto Encyclopedia of Genes and Genomes database. The analysis answers a focused question: among the genes that changed expression in your experiment, which known molecular pathways contain more of those genes than expected by chance? This guide explains the structure of KEGG, the steps required to run enrichment analysis on RNA-seq data, and the biological reasoning needed to interpret the output without overstating what the statistics show.

The intended reader is a biology student, researcher, or laboratory professional who has completed differential expression analysis and now faces a list of differentially expressed genes with no clear next step. The practical outcome is a defensible pathway interpretation that supports a manuscript, grant application, or experimental follow-up. The guide assumes you have a count matrix or gene list ready and focuses specifically on KEGG instead of covering all functional enrichment tools.

What KEGG Is and How the Database Is Organized

KEGG is a reference database that organizes biological information into pathway maps, molecular interaction networks, and hierarchical classifications of genes and proteins. Unlike a simple gene annotation list, KEGG pathways represent manually curated diagrams of molecular interactions and reactions. Each pathway has a unique identifier, such as hsa04010 for the human MAPK signaling pathway or hsa04910 for insulin signaling. The pathway identifiers use a species prefix, so mmu indicates mouse, rno indicates rat, and hsa indicates human.

The database structure matters for enrichment analysis because the statistical test depends on how genes are assigned to pathways. A gene can appear in multiple pathways, and pathway sizes vary considerably. Some pathways contain dozens of genes while others contain hundreds. This variation affects the power of the enrichment test and the biological meaning of a significant result. A small pathway with three differentially expressed genes out of twenty total genes may produce a highly significant p-value, but the biological scope of that finding is narrow. A large pathway with thirty differentially expressed genes out of five hundred total genes may be less significant statistically but broader in biological relevance.

KEGG also organizes genes into broader categories called BRITE hierarchies, which include functional hierarchies for pathways, diseases, and organisms. The pathway-focused analysis described in this guide uses the KEGG PATHWAY database specifically. The National Center for Biotechnology Information provides access to related sequence and annotation resources that can help you confirm gene identifiers before running enrichment analysis, since mismatched identifiers are a common source of failed analyses [<a href="#ref-1">1</a>].

Input Data Requirements for KEGG Enrichment

The minimal input for KEGG over-representation analysis is a list of differentially expressed genes and a background gene list. The background list is the set of all genes that were tested for differential expression, which is typically all genes detected in your RNA-seq experiment. Using the correct background is essential because the enrichment test compares the proportion of differentially expressed genes in a pathway against the proportion of all measured genes in that pathway. If you use an incorrect background, such as all genes in the genome instead of all genes detected in your experiment, the enrichment results will be biased.

The differential expression analysis must be completed before enrichment analysis begins. Standard RNA-seq workflows include quality assessment of raw reads, alignment or quantification against a reference transcriptome, generation of a count matrix, and statistical testing for differential expression. The Bioconductor project provides documented packages and workflows for each of these steps, including DESeq2 and edgeR for differential expression and clusterProfiler for enrichment analysis [<a href="#ref-2">2</a>]. The Galaxy Training Network offers accessible tutorials that walk through the complete RNA-seq workflow from raw data to functional interpretation, which is useful if you are learning the process or need a reproducible platform [<a href="#ref-3">3</a>].

The gene identifiers used in your differential expression results must match the identifiers used by the KEGG database. Common identifier types include Entrez Gene IDs, Ensembl gene IDs, and official gene symbols. Most enrichment tools can convert between identifier types, but the conversion step requires an internet connection or a locally installed annotation package. If your count matrix uses Ensembl IDs and your enrichment tool expects Entrez IDs, you must perform the conversion before running the analysis. Failure to convert identifiers correctly produces empty results or misleading enrichment calls.

Over-Representation Analysis Versus Gene Set Enrichment Analysis

Two main statistical approaches are used for KEGG pathway analysis of RNA-seq data. Over-representation analysis tests whether the list of differentially expressed genes contains more genes from a given pathway than expected by chance. This approach requires you to choose a significance threshold for differential expression, such as an adjusted p-value below 0.05 and an absolute log2 fold change above 1. The test is simple and widely used, but it discards information about genes that did not reach the threshold.

Gene set enrichment analysis uses the full ranking of all genes by their differential expression statistic instead of a thresholded list. This approach can detect coordinated changes in a pathway even when individual genes do not reach significance. Gene set enrichment analysis is more sensitive for subtle but consistent pathway-level changes. The choice between the two methods depends on your experimental question. If you have a clear list of differentially expressed genes and want to know which pathways they populate, over-representation analysis is appropriate. If you suspect that a pathway is altered but individual gene changes are modest, gene set enrichment analysis may be more informative.

A study of human airway epithelial cells treated with oil dispersants used both approaches to analyze KEGG pathways. The generally applicable gene set enrichment method identified upregulation of ribosomal biosynthesis, protein processing, Wnt signaling, neurotrophin signaling, and insulin signaling pathways under one dispersant treatment. A separate gene set net correlation analysis identified changes in gene co-expression within several KEGG cancer pathways. The study demonstrates that different statistical approaches can reveal different aspects of pathway perturbation from the same RNA-seq data [<a href="#ref-4">4</a>].

Running KEGG Enrichment Analysis with R and Bioconductor

The most common implementation of KEGG enrichment analysis uses the clusterProfiler package from Bioconductor. The package accepts a vector of differentially expressed gene identifiers, a background vector, and an organism-specific KEGG annotation database. The enrichKEGG function performs over-representation analysis and returns a table of enriched pathways with statistics including the number of genes in the pathway, the number of differentially expressed genes in the pathway, the p-value, and the adjusted p-value.

The workflow begins with loading your differential expression results into R. You need two vectors of gene identifiers. The first vector contains the genes that passed your significance threshold. The second vector contains all genes that were tested. The enrichKEGG function requires the gene identifiers to be in a format that KEGG recognizes, typically Entrez Gene IDs. If your data uses Ensembl IDs, you can convert them using the bitr function from clusterProfiler or an annotation package such as org.Hs.eg.db for human data.

After running enrichKEGG, you should examine the results table carefully. The table includes the pathway ID, pathway description, gene ratio, background ratio, p-value, adjusted p-value, and the list of differentially expressed genes in each pathway. The gene ratio is the proportion of differentially expressed genes in the pathway relative to all differentially expressed genes. The background ratio is the proportion of all measured genes in the pathway relative to all measured genes. Comparing these ratios helps you understand whether the enrichment is driven by a large number of genes or a small pathway.

The Bioconductor project maintains documentation and workflow vignettes for clusterProfiler and related packages, including guidance on handling organism-specific annotations and visualizing enrichment results [<a href="#ref-2">2</a>]. The Carpentries lessons provide foundational training in R programming and data manipulation that is useful before attempting enrichment analysis, since the analysis requires comfort with data frames, vectors, and function calls [<a href="#ref-5">5</a>].

Visualizing KEGG Enrichment Results

Visualization is a necessary step for interpreting enrichment results and communicating them to readers. The most common visualizations are the bar plot, the dot plot, and the pathway diagram. The bar plot shows the number of differentially expressed genes in each enriched pathway, ordered by adjusted p-value. The dot plot shows the gene ratio on the x-axis, the pathway name on the y-axis, and uses point size and color to represent gene count and adjusted p-value. Both plots are generated easily from the enrichment results object in clusterProfiler.

The KEGG pathway diagram is a more detailed visualization that colors the genes in your differential expression list directly on the curated pathway map. The pathview package in Bioconductor generates these diagrams. The colored pathway diagram helps you see which specific genes in a pathway changed expression and where they sit in the molecular interaction network. This visualization is valuable for identifying the direction of change, since you can color up-regulated genes in red and down-regulated genes in green.

A study of flurochloridone-treated rat testes used KEGG enrichment to interpret differentially expressed genes from RNA-seq data. Up-regulated genes were enriched in pathways associated with testicular injury, including MAPK signaling, lysosome, and focal adhesion. Down-regulated genes were enriched in pathways associated with reproductive function, including sexual reproduction, spermatogenesis, and germ cell development. The study also confirmed that the lowest dose group showed gene expression similar to the control group, supporting the no-observed-adverse-effect level. This example shows how KEGG enrichment can separate biological processes by direction of change and link pathway results to toxicological outcomes [<a href="#ref-6">6</a>].

Selecting the Correct Organism and Annotation

KEGG uses organism-specific pathway annotations. The pathway map for human MAPK signaling differs from the mouse MAPK signaling map because the gene sets are not identical. When you run enrichment analysis, you must specify the correct organism code. The three-letter code appears in the pathway identifier, such as hsa for Homo sapiens, mmu for Mus musculus, and rno for Rattus norvegicus. Using the wrong organism code produces results based on the wrong gene sets, which invalidates the analysis.

The organism code must match the species of your RNA-seq samples. If you sequenced human cell lines, use hsa. If you sequenced mouse tissue, use mmu. For less common model organisms, check whether KEGG has a pathway annotation for that species. Some organisms have limited KEGG pathway coverage, which means many genes will not map to any pathway and the enrichment analysis will have reduced power. In this situation, you may need to use a different functional annotation resource or map your genes to a closely related species with better coverage.

A study of sex differentiation in the zig-zag eel used whole-transcriptome sequencing of male and female gonads and identified thousands of differentially expressed mRNAs and non-coding RNAs. KEGG enrichment analysis identified pathways associated with sex differentiation, including TGF-beta signaling, steroid hormone biosynthesis, Hippo signaling, and Wnt signaling. The study also constructed a competing endogenous RNA network showing that sex differentiation genes are regulated by multiple lncRNA and circRNA pairs. This example illustrates the use of KEGG enrichment in a non-model organism and the integration of pathway results with other regulatory layers [<a href="#ref-7">7</a>].

Interpreting Adjusted P-Values and Effect Sizes

The output of KEGG enrichment analysis includes raw p-values and adjusted p-values. The adjusted p-value accounts for multiple testing, since you are testing hundreds of pathways simultaneously. The Benjamini-Hochberg method is the most common adjustment and controls the false discovery rate. You should report adjusted p-values in your results and use them for deciding which pathways are significant. A common threshold is an adjusted p-value below 0.05, but the appropriate threshold depends on your field and the number of pathways tested.

The p-value alone does not tell you the biological importance of a pathway. A pathway can be statistically significant but biologically trivial if the effect size is small. The gene ratio and the number of differentially expressed genes in the pathway provide effect size information. A pathway with a high gene ratio and many differentially expressed genes is more likely to represent a meaningful biological change than a pathway with a low gene ratio and few genes. You should report both the statistical significance and the effect size in your interpretation.

A study of diabetic kidney disease combined RNA-seq data from mouse models with public datasets from the Gene Expression Omnibus. The researchers identified differentially expressed genes using thresholds of absolute log2 fold change greater than 1 and p-value below 0.05. They then performed GO and KEGG enrichment analysis on the co-expressed genes and validated expression levels with quantitative PCR. The study demonstrates the common workflow of combining multiple datasets, applying consistent thresholds, and using enrichment analysis to identify the biological functions of validated genes [<a href="#ref-8">8</a>].

Common Failure Patterns in KEGG Enrichment Analysis

Several recurring problems cause failed or misleading KEGG enrichment analyses. The most common is identifier mismatch. If your gene list uses gene symbols but the enrichment tool expects Entrez IDs, the analysis will return few or no results. Always verify the identifier type in your input data and convert if necessary. The conversion step should be documented in your methods so that reviewers can reproduce your analysis.

A second common failure is using the wrong background gene list. Some researchers use the entire genome as background when they should use only the genes detected in their experiment. This error inflates the significance of enrichment results because the background ratio is underestimated. The correct background is the set of all genes that were tested for differential expression, which is typically all genes with sufficient read counts in your count matrix.

A third failure is ignoring the direction of gene expression change. Over-representation analysis treats all differentially expressed genes equally, regardless of whether they are up-regulated or down-regulated. This approach can obscure biology when a pathway contains both up-regulated and down-regulated genes that respond to different signals. Running separate enrichment analyses for up-regulated and down-regulated genes often provides clearer biological interpretation. The flurochloridone study used this approach and found that up-regulated genes were enriched in injury pathways while down-regulated genes were enriched in reproductive function pathways [<a href="#ref-6">6</a>].

A fourth failure is overinterpreting pathway names without examining the specific genes. A pathway name such as "Wnt signaling" suggests a coherent biological process, but the differentially expressed genes in that pathway may be peripheral components with unclear functional relevance. Always examine the list of differentially expressed genes in each enriched pathway and consider whether they form a coherent biological story. The oral lichen planus study identified differentially expressed genes related to T cell regulation and inflammation and the Wnt signaling pathway, then validated the top dysregulated genes with quantitative PCR in an independent sample set [<a href="#ref-9">9</a>].

Integrating KEGG Results with Other Functional Analyses

KEGG enrichment is one of several functional analysis methods available for RNA-seq data. Gene Ontology enrichment analysis tests for overrepresentation of genes in biological processes, molecular functions, and cellular components. The two methods provide complementary information. GO terms describe the functions of individual genes, while KEGG pathways describe the interactions between genes in a molecular network. Many studies report both analyses together.

A study of epithelial ovarian cancer used RNA-seq data from the Sequence Read Archive and performed both GO term enrichment and KEGG pathway analysis. The researchers identified 1091 differentially expressed genes and found that ribosome and glycolysis or gluconeogenesis pathways had potential as diagnostic and treatment targets. The study also identified specific genes, including RPL41, ALDH3A2, ERBB2, and ATF4, that were consistently altered across multiple pathways. This example shows how combining GO and KEGG analyses with gene-level inspection produces a more complete biological interpretation [<a href="#ref-10">10</a>].

A study of 4-octyl itaconate effects on myogenic differentiation used RNA-seq followed by GO, KEGG, and gene set enrichment analysis. The KEGG enrichment and gene set enrichment results showed that the compound affected multiple metabolic pathways during myogenic differentiation, including PI3K-Akt signaling, calcium signaling, and PPAR signaling. The study also used a PI3K-Akt activator to explore the role of that pathway in the observed inhibition of differentiation. This example demonstrates the integration of KEGG results with experimental validation to test pathway hypotheses [<a href="#ref-11">11</a>].

Using KEGG Enrichment in Multi-Omics Studies

KEGG enrichment analysis is frequently applied to data from experiments that combine RNA-seq with other molecular measurements. ATAC-seq measures chromatin accessibility and identifies regulatory regions that may control gene expression. When ATAC-seq and RNA-seq data are collected from the same samples, researchers can integrate the two data types to identify genes with both altered chromatin accessibility and altered expression. KEGG enrichment of the integrated gene list can reveal pathways that are regulated at multiple levels.

A study of systemic lupus erythematosus integrated ATAC-seq and RNA-seq data from CD4+ T cells of patients and healthy controls. The researchers identified differentially accessible chromatin regions and differentially expressed genes, then used KEGG pathway enrichment to determine the pathways of interactions between predicted transcription factors and differentially expressed genes. The transcription factor gene interaction networks were enriched predominantly in T helper 17 cell differentiation, the cell cycle, and several signaling pathways. The study also experimentally verified the expression changes of target genes following knockdown of a predicted transcription factor [<a href="#ref-12">12</a>].

A study of the Yili horse mammary gland used paired ATAC-seq and RNA-seq profiling to characterize chromatin accessibility and transcriptional programs during lactation. Up-regulated genes were enriched in biological processes related to cell differentiation, tissue development, and signaling pathways including PI3K-Akt, JAK-STAT, and Rap1 signaling. Integration of the two data types highlighted 22 differentially expressed genes with altered chromatin accessibility, including genes involved in oxytocin signaling, glutathione metabolism, and Wnt signaling. The study presents candidate pathways and genes for further validation [<a href="#ref-13">13</a>].

A study of muscle growth differences between Gannan yak and Jeryak used ATAC-seq and RNA-seq to analyze chromatin openness and gene expression in longissimus dorsi muscle. GO and KEGG enrichment analysis indicated that differential peak-associated genes are involved in the negative regulation of muscle adaptation and the Hippo signaling pathway. Integration analysis revealed overlapping genes significantly enriched during skeletal muscle cell differentiation and muscle organ morphogenesis. The study screened FOXO1, ZBED6, CRY2, and CFL2 as potentially involved in skeletal muscle development [<a href="#ref-14">14</a>].

KEGG Enrichment in Single-Cell RNA-Seq Analysis

Single-cell RNA-seq generates expression measurements for thousands of individual cells, which requires different analytical approaches than bulk RNA-seq. After clustering cells and identifying cell types, researchers often perform differential expression analysis between conditions within specific cell types. The resulting gene lists can be analyzed with KEGG enrichment to identify pathways that are altered in specific cell populations.

A study of periodontitis integrated single-cell RNA-seq and bulk RNA-seq data to identify necroptosis-related genes as therapeutic targets. The researchers identified cell types in periodontitis tissues, observed changes in cell population abundance, and selected necroptosis-related genes differentially expressed in specific cell populations. Neutrophils and macrophages showed higher necroptosis scores. KEGG and GO enrichment analysis was performed on the differentially expressed genes, followed by machine learning to identify hub genes associated with the inflammatory response [<a href="#ref-15">15</a>].

A study of oxygen-induced retinopathy used single-cell RNA-seq data from retinal tissues of mice under normoxic and oxygen-induced retinopathy conditions. The analysis identified ten retinal cell types and used pathway enrichment analysis including KEGG to identify significantly up-regulated and down-regulated genes. The study identified potential target genes and explored how mutations of specific genes influence disease development. The single-cell analysis suggested that several genes may be potential targets in the pathogenesis of oxygen-induced retinopathy [<a href="#ref-16">16</a>].

A study of gastric cancer integrated single-cell RNA-seq and bulk RNA-seq data to identify targets of cisplatin and saikosaponin D combination treatment. GO and KEGG enrichment analysis revealed that the FAK/AKT/mTOR signaling pathway was most strongly correlated with the differentially expressed genes. Single-cell analysis showed that specific genes were expressed mainly in particular cell types, such as monocytes and smooth muscle cells. Network pharmacology and molecular docking analysis identified the PI3K-Akt signaling pathway as the most likely target of the compound [<a href="#ref-17">17</a>].

Reproducibility and Documentation of KEGG Enrichment Analysis

Reproducibility is a core requirement for bioinformatics analyses. Your KEGG enrichment analysis should be reproducible by another researcher who has access to your data and code. This requires documenting the software versions, the reference database version, the gene identifier conversion method, and the statistical parameters used in the analysis. The Bioconductor project emphasizes reproducible genomic analysis and provides versioned packages and workflow documentation [<a href="#ref-2">2</a>].

The nf-core community provides standards for building reproducible bioinformatics pipelines, including guidelines for containerization, version control, and documentation [<a href="#ref-18">18</a>]. While nf-core pipelines may not include KEGG enrichment as a standard step, the principles of pipeline design apply to your analysis workflow. You should record the exact version of the KEGG database used, since pathway annotations change over time and results from different database versions are not directly comparable.

A reproducible RNA-seq workflow from raw sequencing reads to pathway enrichment was described in a study that integrated SRA Toolkit, FastQC, MultiQC, Salmon, tximport, DESeq2, and clusterProfiler. The workflow used a decoy-aware reference transcriptome and defined differentially expressed genes using an adjusted p-value below 0.05 and absolute shrunken log2 fold change of at least 1. The study demonstrated that the workflow produced publication-quality figures and source data, and that transcript-level estimates were summarized to gene-level counts for downstream analysis [<a href="#ref-19">19</a>].

The EMBL-EBI Training program provides learning pathways for bioinformatics data resources and practical analysis education [<a href="#ref-20">20</a>]. These training materials are useful for researchers who need to build the computational skills required for reproducible RNA-seq analysis, including working with reference databases, running analysis tools, and documenting workflows.

Records and Measurements for KEGG Enrichment Analysis

Maintaining clear records of your enrichment analysis is essential for manuscript preparation and for troubleshooting failed analyses. The following records should be kept for each enrichment analysis you run.

The enrichment results table should be saved as a plain text file or CSV so that it can be examined without rerunning the analysis. The table should include all pathways tested, also the significant ones, because reviewers may ask about the complete set of results. The gene lists for each enriched pathway should also be saved so that you can examine the specific genes driving each enrichment signal.

At a Glance: KEGG Enrichment Analysis Decision Table

Practical Implementation Steps for KEGG Enrichment Analysis

The following steps provide a practical workflow for performing KEGG enrichment analysis on your RNA-seq data. Each step includes a concrete decision point and a check for common errors.

Step 1: Prepare your differential expression results. Export the results table from your differential expression analysis tool, such as DESeq2 or edgeR. The table should contain gene identifiers, log2 fold changes, and adjusted p-values for all tested genes. Verify that the gene identifiers are consistent throughout the table.

Step 2: Define your differentially expressed gene list. Apply your significance thresholds to select the genes for enrichment analysis. A common threshold is an adjusted p-value below 0.05 and an absolute log2 fold change above 1. Record the number of genes that pass the threshold. If the number is very small, below approximately 50 genes, consider whether enrichment analysis will be informative or whether you should use gene set enrichment analysis instead.

Step 3: Define your background gene list. The background list should contain all genes that were tested for differential expression. This is typically all genes with sufficient read counts in your count matrix. Do not use the entire genome as background unless your experiment measured all genes with equal sensitivity.

Step 4: Convert gene identifiers if needed. Check whether your gene identifiers match the format expected by the enrichment tool. If you are using clusterProfiler, convert your identifiers to Entrez Gene IDs using the bitr function. Verify that the conversion succeeded for most of your genes. A conversion rate below 70 percent indicates a problem with your identifier format or your annotation package.

Step 5: Run the enrichment analysis. Use the enrichKEGG function with your differentially expressed gene list, your background list, and your organism code. Save the full results table to a file. Examine the number of enriched pathways at your chosen adjusted p-value threshold. If no pathways are enriched, check your gene identifiers and background list before concluding that your gene list has no pathway enrichment.

Step 6: Examine the enriched pathways. For each significant pathway, record the pathway ID, the number of differentially expressed genes in the pathway, the gene ratio, and the adjusted p-value. Examine the list of differentially expressed genes in each pathway to confirm that they are biologically relevant to your experimental system.

Step 7: Generate visualizations. Create a bar plot or dot plot of the enriched pathways. Generate pathway diagrams for the top two or three pathways using pathview. Save all figures at publication resolution.

Step 8: Document your analysis. Record the software versions, database versions, and parameters used in the analysis. Write a methods paragraph that describes the enrichment analysis in sufficient detail for another researcher to reproduce it.

Common Failure Patterns and Troubleshooting

Limitations of KEGG Enrichment Analysis

KEGG enrichment analysis has several limitations that affect the interpretation of results. The KEGG database is manually curated and does not cover all genes in any organism. Genes without pathway annotations are excluded from the analysis, which means the enrichment test only considers a subset of your differentially expressed genes. If a large fraction of your differentially expressed genes lack KEGG annotations, the enrichment results may not represent the full biological response in your experiment.

The pathway definitions in KEGG are static representations of molecular interactions. They do not capture cell-type-specific or condition-specific pathway activity. A pathway that is enriched in your gene list may not be active in your experimental system, and a pathway that is not enriched may still be functionally important. The enrichment test identifies statistical overrepresentation, not biological activity. You need additional evidence, such as protein-level measurements or functional assays, to confirm that an enriched pathway is actually altered in your system.

The statistical power of enrichment analysis depends on the number of differentially expressed genes and the size of the pathway. Small pathways require a larger proportion of differentially expressed genes to reach significance. Large pathways may reach significance with a small proportion of differentially expressed genes, but the biological relevance of the finding may be unclear. You should interpret enrichment results in the context of the number of genes driving the signal and the coherence of those genes within the pathway.

A study of liver fibrosis induced by polystyrene microplastics used GO enrichment and KEGG pathway analysis to show that differentially expressed genes were mainly enriched in lipid metabolism. The study identified four hub genes using network analysis and confirmed that three of these genes were significantly augmented under chronic exposure. The study demonstrates that KEGG enrichment can identify a metabolic theme, but the specific genes and their functional roles required additional network analysis and experimental validation [<a href="#ref-21">21</a>].

A study of muscle growth in three pig breeds used RNA-seq to identify differentially expressed genes in longissimus dorsi muscle and performed GO enrichment, KEGG pathway enrichment, and protein-protein interaction network analysis. The study identified hundreds of differentially expressed genes between breeds and used the enrichment analyses to understand the molecular mechanisms underlying muscle growth differences. This example shows the application of KEGG enrichment in agricultural species and the integration of enrichment results with network analysis [<a href="#ref-22">22</a>].

Safety and Regulatory Context for KEGG Enrichment Analysis

KEGG enrichment analysis is a computational method that does not involve direct safety risks to researchers or research subjects. The primary risks are interpretive and reputational. Misleading enrichment results can lead to incorrect biological conclusions, wasted experimental effort, and retractions if the errors are discovered after publication. The regulatory context for RNA-seq studies depends on the source of the samples and the intended use of the results. Studies involving human subjects require ethical approval and informed consent. Studies involving animal subjects require institutional animal care and use committee approval. Studies intended to support drug development or clinical decisions are subject to additional regulatory requirements.

The computational nature of the analysis does not eliminate the need for rigorous quality control. You should verify that your differential expression results are based on high-quality RNA-seq data with adequate sequencing depth, appropriate alignment rates, and sufficient biological replication. The Galaxy Training Network provides tutorials on RNA-seq quality control and analysis that are useful for establishing these quality checks [<a href="#ref-3">3</a>]. The EMBL-EBI Training program offers learning pathways for data resources and practical analysis education that cover quality assessment and data management [<a href="#ref-20">20</a>].

Professional Escalation Criteria for KEGG Enrichment Results

You should seek additional expertise or escalate your analysis when you encounter specific situations that exceed your current capacity to interpret results. The following criteria indicate when consultation with a bioinformatics specialist, a biostatistician, or a domain expert is appropriate.

Frequently Asked Questions

What is the difference between KEGG enrichment and GO enrichment?

KEGG enrichment tests whether differentially expressed genes are overrepresented in curated molecular pathway maps, which show interactions between genes and gene products. GO enrichment tests whether differentially expressed genes are overrepresented in terms describing biological processes, molecular functions, and cellular components. KEGG pathways emphasize the network context of gene products, while GO terms describe the functions and locations of individual gene products. Many studies report both analyses because they provide complementary perspectives on the biological meaning of differential expression.

How many differentially expressed genes do I need for KEGG enrichment analysis?

There is no fixed minimum number of differentially expressed genes required for KEGG enrichment analysis, but the statistical power increases with the number of genes. With fewer than 50 differentially expressed genes, the enrichment test may lack power to detect pathway overrepresentation, especially for small pathways. With very small gene lists, you should consider whether enrichment analysis is appropriate or whether you should examine the genes individually. The background gene list should always contain all genes tested for differential expression, regardless of the number of differentially expressed genes.

Should I use adjusted p-values or raw p-values for KEGG enrichment results?

You should use adjusted p-values for deciding which pathways are significantly enriched. The adjustment accounts for the multiple testing problem that arises from testing hundreds of pathways simultaneously. The Benjamini-Hochberg method is the most common adjustment and controls the false discovery rate. Report both raw and adjusted p-values in your results so that readers can assess the statistical evidence. A pathway with a raw p-value below 0.05 but an adjusted p-value above 0.05 should not be reported as significant.

Can I run KEGG enrichment analysis without R programming skills?

Several web-based and graphical tools provide KEGG enrichment analysis without requiring R programming. The Galaxy platform offers a web-based interface for bioinformatics analysis, including functional enrichment, and the Galaxy Training Network provides tutorials for these tools [<a href="#ref-3">3</a>]. DAVID is a web-based tool that provides KEGG pathway analysis and was used in the flurochloridone study [<a href="#ref-6">6</a>]. However, learning basic R skills is recommended because R-based tools such as clusterProfiler offer more flexibility and reproducibility. The Carpentries lessons provide foundational R training for researchers [<a href="#ref-5">5</a>].

Why did my KEGG enrichment analysis return no significant pathways?

The most common cause of empty enrichment results is gene identifier mismatch. If your gene list uses gene symbols or Ensembl IDs but the enrichment tool expects Entrez IDs, the analysis will map few or no genes to pathways. Check your identifier format and convert if necessary. Another common cause is an incorrect background gene list. If the background contains genes that were not measured in your experiment, the enrichment test may be biased. Verify that your background contains all genes tested for differential expression.

How do I choose between over-representation analysis and gene set enrichment analysis?

Over-representation analysis is appropriate when you have a clear list of differentially expressed genes defined by significance thresholds and you want to know which pathways contain more of those genes than expected by chance. Gene set enrichment analysis is appropriate when you want to use the full ranking of all genes by their differential expression statistic and detect coordinated pathway changes that may not reach individual gene significance. The choice depends on your experimental question and the nature of your data. Some studies use both approaches to gain complementary perspectives, as demonstrated in the oil dispersant study [<a href="#ref-4">4</a>].

What should I do if my enriched pathways do not match the expected biology of my system?

First, verify that your differential expression analysis is correct by checking the quality of your RNA-seq data, the adequacy of your biological replication, and the appropriateness of your statistical model. Second, verify that your gene identifiers and background list are correct. Third, examine the specific genes driving the enrichment in each pathway to determine whether the pathway signal is coherent or driven by a few genes with unrelated functions. If the enrichment results remain inconsistent with known biology, consult a bioinformatics specialist or a domain expert to review your analysis.

How should I report KEGG enrichment results in a manuscript?

Report the software and version used for the analysis, the KEGG database version, the gene identifier type, the background gene list definition, the significance thresholds for differential expression, and the adjusted p-value threshold for enrichment significance. Present the enriched pathways in a table with pathway IDs, descriptions, gene counts, gene ratios, and adjusted p-values. Include a visualization such as a dot plot or bar plot. Describe the biological interpretation of the enriched pathways in the context of your experimental system, and note the limitations of the analysis.

Related Bioinformatics Guides

• How to Interpret Gene Set Enrichment Analysis Results
• Gene Set Enrichment Analysis in R: A Practical Tutorial for Interpreting Omics Data
• Pathway Enrichment Analysis for Proteomics: Tools and Interpretation
• Pathway Enrichment Analysis in R: Tools and Visualization for Omics Interpretation
• RNA-Seq Data Analysis in Galaxy: A User-Friendly Platform

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References and Further Reading

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.