How to Visualize Enrichment Analysis Results: Dot Plots, Bar Plots, and Enrichment Maps for RNA-seq

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

Key Takeaways

• Dot plots are optimal for comparing enrichment across multiple gene lists or conditions, effectively encoding gene ratio, adjusted p-value, and gene count in a single visualization, though they can become cluttered with an excessive number of terms.
• Bar plots offer simplicity and are best suited for presenting a small number of top enriched terms, providing an easily interpretable visualization of gene count or negative log adjusted p-value, but with limited information density per term.
• Enrichment maps are crucial for revealing relationships and overlap between enriched gene sets, visualizing these connections as a network where nodes represent terms and edges signify shared genes, thereby exposing clusters of related pathways.
• The choice of visualization method is dictated by the number of enriched terms, the experimental design (e.g., multi-condition time course), and the primary analytical question, with dot plots for multi-dimensional comparison, bar plots for simplicity, and enrichment maps for pathway interrelationships.
• Reproducibility in enrichment visualization hinges on meticulous documentation, including recording software versions, parameters (gene list definition, background set, significance thresholds), and gene annotation database versions, alongside saving figure generation scripts and utilizing version control.
• Common pitfalls in enrichment visualization include overcrowded figures, inconsistent thresholds across comparisons, ignoring gene set overlap, misinterpreting gene ratio, and poor color choices, all of which can be mitigated by limiting displayed terms, standardizing parameters, employing enrichment maps, considering both gene ratio and count, and using perceptually uniform, colorblind-safe palettes.

Enrichment analysis converts a long list of differentially expressed genes from an RNA-seq experiment into interpretable biological themes. The output is typically a table of gene ontology terms, KEGG pathways, or other gene sets with associated statistics. A table alone does not communicate the magnitude, significance, or overlap of enriched terms effectively. This article explains how to choose and construct dot plots, bar plots, and enrichment maps for publication-quality figures, with practical guidance on data preparation, software options, interpretation, and common pitfalls. The focus is on researchers who have completed differential expression analysis and need to present enrichment results clearly and reproducibly.

At a Glance

The table below summarizes the main visualization methods covered in this article, their best use cases, and key considerations for each.

Understanding Enrichment Analysis Outputs Before Visualization

Enrichment analysis identifies biological pathways that are overrepresented in a gene list more than would be expected by chance. The input is typically a list of differentially expressed genes from an RNA-seq experiment, and the output is a set of statistically enriched terms from databases such as Gene Ontology or KEGG. A complete RNA-seq analysis involves multiple tools and substantial computational requirements, and the Galaxy platform simplifies this process by embedding needed tools in a web interface while providing reproducibility [<a href="#ref-1">1</a>]. Before creating any figure, you must understand the structure of your enrichment results and the statistics that accompany each term.

Core Statistics in Enrichment Results

Every enrichment result table contains several columns that determine how you should visualize the data. The gene ratio is the proportion of genes from your input list that map to a given term. The adjusted p-value, often calculated using the Benjamini-Hochberg method, controls the false discovery rate across multiple testing. The gene count indicates how many of your input genes belong to each term. Some tools also report a z-score or enrichment score that reflects whether the term is up-regulated or down-regulated in your dataset.

The choice of threshold for significance is a critical decision. Research on gene ontology enrichment analysis reproducibility shows that setting appropriate thresholds in data processing is essential for improving reproducibility and accuracy, and combining different GO methods can avoid the limitations of each one [<a href="#ref-2">2</a>]. Different software and methods can lead to different conclusions, and there is no universal agreement on standards and processes for analysis [<a href="#ref-2">2</a>]. When preparing figures, you should report the thresholds you used and consider whether your conclusions hold across different threshold settings.

Gene List Definition and Its Impact on Visualization

The gene list you feed into enrichment analysis determines what your figures will show. For differential expression results, you must decide on cutoffs for fold change and adjusted p-value. For ranked gene lists used in gene set enrichment analysis, the ranking metric matters. The protocol for pathway enrichment analysis defines three major steps: defining a gene list from omics data, determining statistically enriched pathways, and visualizing and interpreting the results [<a href="#ref-3">3</a>]. This protocol is designed for biologists with no prior bioinformatics training and uses freely available software including g:Profiler, GSEA, Cytoscape, and EnrichmentMap [<a href="#ref-3">3</a>].

When you change the gene list definition, your enrichment results will change, and so will your figures. A common practice is to test several thresholds and verify that the top enriched terms remain stable. If the top terms shift dramatically with small changes in thresholds, your results may not be robust, and you should report this limitation.

Choosing the Right Visualization for Your Data

The choice of visualization depends on the number of enriched terms, the number of conditions being compared, and the message you want to convey. Dot plots and bar plots are the most common choices for publication figures, while enrichment maps provide a network perspective that is valuable for understanding pathway relationships.

Dot Plots for Multi-Dimensional Comparison

Dot plots are the workhorse of enrichment visualization because they encode three variables in a single figure. The x-axis shows the gene ratio, the y-axis lists the enriched terms, the dot size represents the number of genes, and the dot color indicates the adjusted p-value. This format allows readers to quickly identify terms with high gene ratios and strong statistical significance.

For RNA-seq experiments comparing multiple conditions, dot plots can be faceted or grouped to show enrichment patterns side by side. When you have many significant terms, you should limit the plot to the top 10 to 20 terms by adjusted p-value or gene ratio to maintain readability. The clusterProfiler package in R is a common tool for generating dot plots, and Bioconductor provides official documentation for package installation and reproducible genomic analysis workflows [<a href="#ref-4">4</a>].

Bar Plots for Simplicity and Accessibility

Bar plots present enrichment results with the simplest possible visual encoding. The y-axis lists the enriched terms, and the x-axis shows either the gene count or the negative logarithm of the adjusted p-value. Bar plots are appropriate when you have a small number of terms to present, typically fewer than 15, and when your audience may not be familiar with dot plot conventions.

A common variation is the horizontal bar plot ordered by adjusted p-value, with bars colored by significance level or by gene ontology category. This format is easy to read in presentations and posters. However, bar plots convey less information per term than dot plots because they cannot simultaneously show gene ratio, gene count, and significance. For supplementary figures with many terms, a bar plot becomes unwieldy, and a dot plot or heatmap is preferable.

Enrichment Maps for Pathway Relationships

Enrichment maps address a limitation of dot plots and bar plots: they do not show how enriched terms relate to each other. Many gene ontology terms share genes, and pathways often overlap in their membership. An enrichment map visualizes these relationships as a network where nodes are enriched terms and edges connect terms that share a significant number of genes.

The EnrichmentMap protocol, implemented in Cytoscape, provides a practical approach to building these networks. The complete protocol can be performed in approximately 4.5 hours and is designed for use by biologists with no prior bioinformatics training [<a href="#ref-3">3</a>]. The workflow involves running enrichment analysis with g:Profiler or GSEA, importing results into Cytoscape, and applying the EnrichmentMap app to construct the network [<a href="#ref-3">3</a>]. The resulting figure shows clusters of related pathways, which can reveal higher-level biological themes that are not apparent from a ranked list of terms.

Enrichment maps are particularly valuable for complex datasets where multiple related pathways are co-regulated. For example, a study of SARS-CoV-2 infection in cardiomyocytes used gene set enrichment analysis, GO analysis, and KEGG pathway analysis to identify the main effects of the virus on cardiomyocytes, finding activation of immuno-inflammatory responses through multiple signaling pathways including TNFα, IL6-JAK-STAT3, and NF-κB [<a href="#ref-5">5</a>]. An enrichment map would show how these pathways cluster and share component genes.

Practical Workflow for Creating Publication-Quality Figures

The workflow for creating enrichment figures follows a consistent sequence: prepare the enrichment results, select the visualization method, generate the figure, and refine the aesthetics for publication. Each step involves decisions that affect the final output.

Step 1: Prepare and Validate Enrichment Results

Before generating any figure, verify that your enrichment results are complete and correctly formatted. Check that the gene identifiers used in the enrichment analysis match the annotation of your input gene list. Confirm that the background gene set is appropriate for your experiment. For RNA-seq data, the background should typically be all genes expressed in your experiment, not all genes in the genome.

Record the software version and parameters used for enrichment analysis. The reproducibility of RNA-seq analysis depends on documenting these details. The Galaxy Training Network provides accessible workflow training and analysis tutorials that emphasize reproducibility context [<a href="#ref-6">6</a>]. Similarly, nf-core documentation describes community pipeline standards for reproducible workflow context [<a href="#ref-7">7</a>]. When you publish figures, you should cite the enrichment tool version and parameters in the methods section.

Step 2: Select Terms for Display

Most enrichment analyses produce dozens or hundreds of significant terms, and you cannot display all of them in a single readable figure. The selection of terms for display is a substantive decision that affects the message of your figure. Common selection criteria include the top N terms by adjusted p-value, terms above a gene ratio threshold, or terms belonging to a specific biological category of interest.

For GO enrichment, you may choose to display only biological process terms, or you may separate results by ontology category. The choice depends on your research question. A study of optic nerve head astrocytes found that gene ontology enrichment analysis of differential expression genes from RNA-seq data indicated that the absence of Piezo1 affects biological processes involving cell division [<a href="#ref-8">8</a>]. In this case, displaying biological process terms would directly support the conclusion about cell cycle regulation.

Step 3: Generate the Figure with Appropriate Software

R packages such as clusterProfiler, available through Bioconductor, provide functions for creating dot plots, bar plots, and enrichment maps. Bioconductor offers official documentation for package installation and reproducible genomic-analysis workflows [<a href="#ref-4">4</a>]. The enrichplot package, which works with clusterProfiler, provides additional visualization functions including tree plots and enrichment maps.

For researchers who prefer web-based tools, several platforms offer enrichment visualization without programming. The Galaxy platform embeds tools for RNA-seq analysis and functional enrichment in its web interface [<a href="#ref-1">1</a>]. NASQAR is a web-based platform for high-throughput sequencing data analysis and visualization [<a href="#ref-9">9</a>]. Confidence is a web application for cross-platform differential gene expression analysis, gene scoring, and enrichment analysis that generates publication-quality figures [<a href="#ref-10">10</a>]. These tools lower the barrier for researchers who do not use R.

Step 4: Refine Aesthetics for Publication

Publication-quality figures require attention to typography, color, and layout. Use a consistent color scale for adjusted p-values across all figures in a manuscript. Choose colorblind-safe palettes. Ensure that axis labels and term names are legible at the final figure size. For dot plots, adjust the figure dimensions so that term names do not overlap.

When you export figures, use vector formats such as PDF or SVG for maximum quality. Raster formats such as PNG are acceptable for supplementary figures but should be exported at sufficient resolution, typically 300 dpi or higher. The specific requirements vary by journal, so check the target journal guidelines before finalizing figures.

Software Options and Their Tradeoffs

The choice of software for enrichment visualization depends on your programming skills, the complexity of your analysis, and your need for reproducibility. Each option has strengths and limitations.

R and Bioconductor Packages

R provides the most flexible and reproducible environment for enrichment visualization. The clusterProfiler package is widely used for GO and KEGG enrichment analysis and includes functions for dot plots, bar plots, and enrichment maps. Bioconductor provides official documentation for package installation and reproducible genomic-analysis workflows [<a href="#ref-4">4</a>]. The GSEPD package is a Bioconductor package for RNA-seq gene set enrichment and projection display [<a href="#ref-11">11</a>].

The main tradeoff is the learning curve. Researchers without R experience must invest time in learning basic syntax and data structures. The Carpentries offers foundational computing and programming lessons that cover R basics [<a href="#ref-12">12</a>]. For life sciences researchers, introductory R training for NGS data analysis provides a starting point for using R in genomics [<a href="#ref-13">13</a>].

Web-Based Platforms

Web-based platforms reduce the programming burden and are suitable for researchers who need results quickly. Galaxy provides a complete environment for RNA-seq analysis from data upload to visualization and functional enrichment analysis [<a href="#ref-1">1</a>]. The Galaxy Training Network offers accessible workflow training and analysis tutorials [<a href="#ref-6">6</a>]. NASQAR provides a web-based platform for high-throughput sequencing data analysis and visualization [<a href="#ref-9">9</a>].

The tradeoff is reduced flexibility. Web platforms typically offer a fixed set of visualization options, and you may not be able to customize figures to the same degree as with R. Reproducibility depends on the platform preserving your analysis history and parameters.

Cytoscape for Enrichment Maps

Cytoscape is the primary tool for creating enrichment maps. The EnrichmentMap protocol uses Cytoscape to construct networks from enrichment results [<a href="#ref-3">3</a>]. Cytoscape provides extensive layout and styling options, making it possible to create publication-quality network figures. The tradeoff is that Cytoscape has a steep learning curve, and network figures require more interpretation from readers than dot plots or bar plots.

Comparing Visualization Methods Across Experimental Contexts

Different experimental designs call for different visualization strategies. The table below provides guidance on matching visualization methods to common RNA-seq experimental scenarios.

Single-Cell RNA-seq Considerations

Single-cell RNA-seq experiments present unique visualization challenges because enrichment analysis is often performed on cluster marker genes or on differentially expressed genes between cell populations. A study of nucleus pulposus cells used single-cell RNA-seq and unsupervised clustering based on gene expression profiles, then performed GO and KEGG analyses that discovered ferroptosis pathways were enriched [<a href="#ref-14">14</a>]. The enrichment results were validated in a rat model of disc degeneration [<a href="#ref-14">14</a>]. For such studies, enrichment maps are particularly useful because they show how pathways relate across multiple cell clusters.

Another single-cell study of hepatocellular carcinoma used scRNA-seq to profile immune cells from tumor and surrounding normal tissues, distinguishing developmentally relevant trajectories, unique immune cell subtypes, and enriched pathways regarding differential genes [<a href="#ref-15">15</a>]. The study demonstrated that FABP1 was overexpressed in tumor-associated macrophages in stage III HCC tissues compared with stage II tissues [<a href="#ref-15">15</a>]. When presenting enrichment results from single-cell data, consider whether the visualization method can accommodate the complexity of multiple cell types and conditions.

Multi-Omics Integration

When enrichment analysis is applied to multiple omics data types, such as combining RNA-seq with methylation data or proteomics, the visualization must accommodate results from different data modalities. The TISCH2 resource provides single-cell RNA-seq data from human and mouse tumors and includes cell-cell communication results, transcription factor analyses, and visualization of top enriched transcription factors for each cell type [<a href="#ref-16">16</a>]. This resource demonstrates how enrichment visualization can be extended beyond standard dot plots and bar plots to include cell-cell communication and transcription factor activity.

For multi-omics studies, consider using separate figures for each data type and then integrating the results in a summary figure. An enrichment map can integrate results from multiple data types by coloring nodes according to the data source. This approach allows readers to see which pathways are supported by multiple lines of evidence.

Common Failure Patterns and How to Avoid Them

Several recurring problems undermine the quality and interpretability of enrichment figures. Recognizing these patterns helps you avoid them in your own work.

Overcrowded Figures

The most common failure is displaying too many terms in a single figure. When a dot plot or bar plot contains more than 20 terms, the labels overlap, the dots become indistinguishable, and the figure is difficult to read. The solution is to limit the display to the most significant or most biologically relevant terms. You can provide the full results table as a supplementary file.

Inconsistent Thresholds Across Comparisons

When comparing enrichment results across conditions, you must apply the same thresholds to all conditions. If you use different adjusted p-value cutoffs for different conditions, the comparison is not valid. The reproducibility of GO enrichment analysis depends on setting appropriate thresholds in data processing [<a href="#ref-2">2</a>]. Document the thresholds and apply them consistently.

Ignoring Gene Set Overlap

Dot plots and bar plots treat each enriched term as independent, but gene sets overlap substantially. Two terms may appear significant because they share the same core genes. An enrichment map reveals these overlaps and helps you identify the underlying biological theme. If you present only a dot plot, you may overstate the number of independent findings.

Misinterpreting Gene Ratio

The gene ratio is the proportion of your input genes that map to a term, not the proportion of the term that is covered by your genes. A term with a high gene ratio may still represent only a small fraction of the genes in that pathway. When interpreting enrichment results, consider both the gene ratio and the absolute gene count.

Poor Color Choices

Color scales that are not perceptually uniform can mislead readers. The default rainbow color scale is problematic because it is not perceptually uniform and is not accessible to colorblind readers. Use a sequential color scale for adjusted p-values, such as a gradient from dark to light, and verify that the scale is legible when printed in grayscale.

Inconsistent Term Naming Across Databases

Different enrichment tools may use different naming conventions for the same biological term. For example, one tool may report "inflammatory response" while another reports "immune response". Before combining results from multiple tools, standardize term names to avoid duplicate or conflicting entries in your figures.

Records and Measurements for Reproducible Figures

Reproducibility requires documenting the complete analysis pathway from raw data to final figures. For enrichment visualization, the following records should be maintained.

Analysis Documentation

Record the version of every software tool used, including the enrichment analysis tool, the visualization package, and the operating system. Record the parameters used for enrichment analysis, including the gene list definition, the background set, the significance threshold, and the multiple testing correction method. Record the date of analysis and the version of the gene annotation database.

The importance of documentation is underscored by research showing that the description of enrichment analyses in previous research is often brief, causing difficulties in both research reproducibility and manuscript review [<a href="#ref-2">2</a>]. Standardized documentation practices would promote advances in biological research [<a href="#ref-2">2</a>].

Figure Generation Scripts

For R-based analyses, save the scripts that generate each figure. The scripts should be self-contained, reading the enrichment results from a file and producing the figure without manual intervention. This practice ensures that figures can be regenerated if the analysis is updated or if reviewers request changes.

Version Control

Use version control for analysis scripts and documentation. The Carpentries offers lessons on Git and version control that are applicable to bioinformatics workflows [<a href="#ref-12">12</a>]. Version control allows you to track changes to your analysis and revert to previous versions if needed.

Parameter Logs

Maintain a log of all parameters used in the analysis, including the exact commands run and the output file names. This log should be stored alongside the analysis scripts and should be referenced in the methods section of any manuscript.

Quality Controls for Enrichment Visualization

Quality control should be applied at multiple stages of the visualization process to ensure that figures accurately represent the underlying data.

Verify Gene Identifier Consistency

Mismatched gene identifiers are a common source of errors in enrichment analysis. If your differential expression results use Ensembl gene IDs but your enrichment tool expects Entrez IDs, the analysis will fail or produce incorrect results. Verify that the identifier mapping is correct before generating figures.

Check for Duplicate Terms

Some enrichment tools report the same biological term under different names or with different database accessions. Check for duplicates in your results and consolidate them before visualization. Duplicate terms inflate the apparent number of findings and can confuse readers.

Validate Against Known Biology

Before finalizing figures, check that the top enriched terms are consistent with the biology of your experimental system. If your experiment involves immune cells, immune response terms should appear. If they do not, there may be an error in the gene list or the enrichment analysis. A study of ducks infected with a gastric nematode found enrichment in immune response, extracellular matrix organization, and chemotaxis and cytokine-mediated signaling pathways, which is consistent with systemic immune activation and tissue remodeling [<a href="#ref-17">17</a>]. Your results should show similar face validity.

Confirm Statistical Significance

Verify that the adjusted p-values in your figure match the values in your enrichment results table. Transcription errors can occur when manually creating figures. Automated figure generation from the results table eliminates this source of error.

Validate Enrichment Results with Independent Methods

When possible, validate key enrichment findings with independent experimental methods. A study of diabetic kidney disease used RNA-seq data combined with GEO datasets, performed GO and KEGG enrichment analysis, and then verified the expression levels of co-expression genes using qRT-PCR [<a href="#ref-18">18</a>]. This validation step strengthens the confidence in enrichment results and provides additional evidence for the biological relevance of the findings.

Limitations of Enrichment Visualization

Enrichment figures have inherent limitations that you should acknowledge when interpreting and presenting results.

Dependence on Database Annotations

Enrichment analysis depends on the completeness and accuracy of gene ontology and pathway annotations. Genes that are poorly annotated will not appear in enrichment results even if they are biologically important. The quality of annotations varies across species and gene families.

Sensitivity to Gene List Definition

The results of enrichment analysis are sensitive to the definition of the input gene list. Small changes in fold change or p-value thresholds can change the set of differentially expressed genes and therefore the enriched terms. Research on bulk RNA-seq differential expression and enrichment analysis found that results from underpowered experiments are unlikely to replicate well, and low replicability does not necessarily imply low precision of results [<a href="#ref-19">19</a>]. For cohorts with more than five replicates, 10 out of 18 data sets achieved high median precision despite low recall and replicability [<a href="#ref-19">19</a>]. This finding highlights the importance of adequate sample sizes for reproducible enrichment results.

Overrepresentation of Well-Studied Pathways

Well-studied pathways have more complete annotations and are more likely to appear as enriched. This bias means that enrichment results may overrepresent well-characterized biology and underrepresent novel or poorly studied processes. The absence of a term from your enrichment results does not mean the process is not involved.

Multiple Testing Burden

Enrichment analysis tests thousands of terms simultaneously, and multiple testing correction is essential. The adjusted p-value controls the false discovery rate, but it also reduces statistical power. With small gene lists or small sample sizes, few terms may remain significant after correction.

Visualization Simplification

All visualization methods simplify the underlying data. Dot plots and bar plots show only selected terms and statistics, while enrichment maps require threshold choices for node inclusion and edge definition. These simplifications can obscure important details, so you should always provide the full enrichment results table as a supplement.

Safety and Regulatory Context for Bioinformatics Tools

Bioinformatics analysis is primarily a computational activity, but it has implications for research integrity and data management that warrant attention.

Data Management and Privacy

RNA-seq data from human subjects may contain sensitive information. Ensure that your data handling complies with institutional review board requirements and applicable privacy regulations. The NCBI provides data resources and search systems for sequence data, and researchers should follow the data submission and access policies of the repositories they use [<a href="#ref-20">20</a>]. The EMBL-EBI provides training on data resources and practical analysis education [<a href="#ref-21">21</a>].

Reproducibility Requirements

Many journals now require that analysis scripts and data be deposited in public repositories. The Galaxy Training Network emphasizes reproducibility context in its training materials [<a href="#ref-6">6</a>]. The nf-core documentation describes community pipeline standards for reproducible workflow context [<a href="#ref-7">7</a>]. Plan for data and code deposition when you design your analysis.

Software Licensing

R and Bioconductor packages are open source, but some web platforms and commercial tools have licensing restrictions. Verify that you have the right to use the software for your analysis and to publish figures generated with it.

Computational Resource Considerations

RNA-seq analysis requires substantial software and computational resources [<a href="#ref-1">1</a>]. When planning your visualization workflow, consider whether your local computing environment can support the required tools. Web-based platforms such as Galaxy can reduce the local computational burden by embedding tools in a web interface [<a href="#ref-1">1</a>].

Professional Escalation Criteria

Some situations require consultation with a bioinformatics specialist or biostatistician. Recognize these situations and escalate appropriately.

Persistent Identifier Mismatches

If you cannot resolve gene identifier mismatches despite following standard procedures, consult a bioinformatics specialist. Identifier mapping errors can invalidate enrichment results, and a specialist can diagnose the source of the problem.

Inconsistent Results Across Tools

If different enrichment tools produce substantially different results from the same gene list, consult a biostatistician. Research shows that different software and methods can lead to different conclusions in GO enrichment analysis [<a href="#ref-2">2</a>]. A specialist can help you determine whether the discrepancies reflect methodological differences or errors in your analysis.

Unexpected Enrichment Patterns

If your enrichment results contradict established biology in your field, do not assume the biology is wrong. First verify your analysis pipeline, then consult with colleagues who have domain expertise. A study of intervertebral disc degeneration used single-cell RNA-seq and found that GO and KEGG analyses discovered that ferroptosis pathways were enriched, and this finding was validated in a rat model [<a href="#ref-14">14</a>]. The validation step is critical for unexpected findings.

Reproducibility Failures

If you cannot reproduce your own enrichment results when you rerun the analysis, document the discrepancy and consult a bioinformatics specialist. Reproducibility failures often indicate undocumented parameters or software version changes.

Complex Multi-Omics Integration

If your study requires integrating enrichment results from multiple omics data types or from single-cell and bulk RNA-seq data, consider consulting a bioinformatics specialist early in the analysis design. The complexity of such integrations often requires specialized expertise in data harmonization and visualization.

A Decision Framework for Matching Enrichment Visualizations to Experimental Questions

Selecting a visualization method is not a matter of preference. The choice should follow directly from the experimental question you need to answer and the audience that will interpret the figure. This section provides a structured decision framework that connects your research objective to a specific visualization type, along with a record system for tracking visualization decisions and a troubleshooting method for common figure failures.

Define the Primary Analytical Question First

Before opening R or Cytoscape, write down the single question your figure must answer. This question determines the visualization method. The table below maps common analytical questions to recommended visualization approaches.

The question should be specific enough that a reader can infer it from the figure caption alone. If the figure does not answer the stated question, the visualization method is wrong regardless of how polished it looks.

A Three-Step Selection Procedure

Use this procedure to select a visualization method systematically instead of by habit.

Step 1: Count your significant terms. Run your enrichment analysis and record the number of terms passing your adjusted p-value threshold. If you have fewer than 15 terms, a bar plot is sufficient. If you have 15 to 50 terms, a dot plot provides better information density. If you have more than 50 terms, consider an enrichment map or heatmap to reveal structure instead of listing individual terms.

Step 2: Determine whether term overlap matters for your conclusion. If your manuscript claims that multiple pathways are independently involved in your condition, you must verify that these pathways do not share the same core genes. An enrichment map shows this overlap directly. If your conclusion is simply that a specific pathway is enriched, a dot plot or bar plot is adequate. Research on pathway enrichment analysis emphasizes that identifying biological pathways enriched in a gene list more than expected by chance is the core goal, and visualization should support interpretation of those results [<a href="#ref-3">3</a>].

Step 3: Match the figure to the journal format. Review the target journal's figure guidelines before generating the figure. Some journals prefer single-panel figures for main text and reserve multi-panel or network figures for supplements. If the journal limits main-text figures to six panels, you may need to use a bar plot for the main text and provide a dot plot or enrichment map as a supplementary figure.

Record System for Visualization Decisions

Reproducibility of figures requires documenting also the software commands but also the reasoning behind visualization choices. Maintain a visualization decision log with the following fields for each figure.

Store this log alongside your analysis scripts. The log serves two purposes. First, it allows you to regenerate the figure exactly if reviewers request changes. Second, it documents that visualization choices were made deliberately instead of arbitrarily. The importance of such documentation is supported by research showing that brief descriptions of enrichment analyses in previous studies cause difficulties in both research reproducibility and manuscript review [<a href="#ref-2">2</a>].

Troubleshooting Method for Figure Failures

When a figure does not communicate the intended message, diagnose the problem systematically instead of adjusting aesthetics randomly. Use this troubleshooting sequence.

Problem 1: The figure looks cluttered but the data are correct. Count the number of terms displayed. If the figure contains more than 20 terms, reduce the display to the top terms by adjusted p-value. If the figure still looks cluttered with fewer than 20 terms, check whether term names are truncated or overlapping. Increase figure dimensions or abbreviate long term names. A dot plot with 15 terms should be readable at single-column width.

Problem 2: The figure does not show expected biological patterns. Verify that the gene identifiers in your enrichment results match the identifiers in your differential expression output. Mismatched identifiers produce enrichment results that do not reflect your actual gene list. Check whether the background gene set is appropriate. For RNA-seq data, the background should be all genes expressed in your experiment, not all genes in the genome. A study of domestic ducks infected with a gastric nematode found enrichment in immune response, extracellular matrix organization, and chemotaxis and cytokine-mediated signaling pathways, which matched the expected biology of systemic immune activation and tissue remodeling [<a href="#ref-17">17</a>]. Your figure should show similar face validity.

Problem 3: The figure is technically correct but reviewers find it confusing. Consider whether the visualization method matches the analytical question. A bar plot cannot show gene ratio and gene count simultaneously, so if reviewers ask about gene overlap, switch to a dot plot or enrichment map. If reviewers ask how pathways relate to each other, an enrichment map is the appropriate response. The EnrichmentMap protocol provides a practical approach to building these networks and can be completed in approximately 4.5 hours by biologists with no prior bioinformatics training [<a href="#ref-3">3</a>].

Problem 4: The figure changes substantially when thresholds are adjusted. This indicates that your enrichment results are not robust to threshold choices. Research on GO enrichment analysis reproducibility found that setting appropriate thresholds in data processing is essential for improving reproducibility and accuracy, and combining different GO methods can avoid the limitations of each one [<a href="#ref-2">2</a>]. Test your visualization with adjusted p-value thresholds of 0.01, 0.05, and 0.1. If the top terms change dramatically across thresholds, report this instability and consider whether your gene list definition is appropriate.

Problem 5: The figure cannot be regenerated from the script. This failure indicates incomplete documentation. The script should read enrichment results from a file and produce the figure without manual intervention. If the script requires manual steps, such as selecting terms by hand, document those steps in the visualization decision log. The Galaxy platform embeds tools for RNA-seq analysis and functional enrichment in a web interface while providing reproducibility [<a href="#ref-1">1</a>]. For R-based workflows, Bioconductor provides official documentation for package installation and reproducible genomic-analysis workflows [<a href="#ref-4">4</a>].

Comparing Visualization Methods on Reproducibility Grounds

Reproducibility varies across visualization methods. Bar plots and dot plots generated from a results table are highly reproducible because the mapping from data to visual encoding is deterministic. Enrichment maps require additional decisions about node inclusion thresholds and edge definition, which introduce more opportunities for variation. If you generate an enrichment map, document the exact parameters used for node filtering and edge construction.

Research on bulk RNA-seq differential expression and enrichment analysis found that results from underpowered experiments are unlikely to replicate well, and low replicability does not necessarily imply low precision of results [<a href="#ref-19">19</a>]. This finding has direct implications for visualization. If your experiment has fewer than five biological replicates per group, your enrichment results may not replicate, and no visualization method can compensate for this limitation. Report the sample size alongside your figures and interpret enrichment patterns cautiously.

When to Use Multiple Visualization Methods Together

A single figure type rarely serves all purposes in a manuscript. A common publication strategy uses a bar plot in the main text to show the top enriched terms, a dot plot in a supplementary figure to show gene ratio and gene count for all significant terms, and an enrichment map in another supplementary figure to show pathway relationships. This combination provides both accessibility and depth.

The decision to use multiple methods should be driven by the manuscript narrative. If the main conclusion is that a specific pathway is enriched, one figure suffices. If the manuscript claims that multiple related pathways are coordinately regulated, an enrichment map is necessary to support that claim. The protocol for pathway enrichment analysis describes visualization and interpretation as the third major step after gene list definition and statistical enrichment determination [<a href="#ref-3">3</a>]. This step should receive the same careful planning as the statistical analysis.

Professional Escalation for Visualization Problems

Some visualization problems require consultation with a bioinformatics specialist. Escalate when you cannot resolve the issue through the troubleshooting sequence above. Specific escalation criteria include persistent identifier mismatches that survive standard correction procedures, inconsistent results across multiple enrichment tools from the same gene list, and enrichment patterns that contradict established biology in your field despite a verified analysis pipeline. Research shows that different software and methods can lead to different conclusions in GO enrichment analysis, and a specialist can help determine whether discrepancies reflect methodological differences or errors in your analysis [<a href="#ref-2">2</a>].

Frequently Asked Questions

What is the difference between over-representation analysis and gene set enrichment analysis?

Over-representation analysis tests whether a list of differentially expressed genes contains more genes from a given pathway than expected by chance. Gene set enrichment analysis uses a ranked list of all genes and tests whether genes from a pathway are concentrated at the top or bottom of the ranking. The choice between them depends on whether you have a discrete list of significant genes or a continuous ranking of all genes.

How many enriched terms should I show in a dot plot?

For a readable figure, limit the display to the top 10 to 20 terms by adjusted p-value or gene ratio. If you have more significant terms, provide the complete results table as a supplementary file. The exact number depends on the length of the term names and the figure dimensions.

Can I use the same visualization for GO and KEGG enrichment results?

Yes, dot plots and bar plots work for both GO and KEGG results. The main difference is that GO terms have a hierarchical structure that can be displayed with a tree plot, while KEGG pathways do not have this structure. Enrichment maps can be built from either type of result.

What is the best way to compare enrichment results across multiple conditions?

Dot plots with faceting by condition are effective for comparing enrichment across conditions. Alternatively, you can create a heatmap where rows are enriched terms and columns are conditions, with color indicating significance or enrichment score. The choice depends on the number of conditions and terms.

How do I create an enrichment map from my results?

The EnrichmentMap protocol uses g:Profiler or GSEA for enrichment analysis, then imports the results into Cytoscape and applies the EnrichmentMap app to construct the network [<a href="#ref-3">3</a>]. The complete protocol takes approximately 4.5 hours and is designed for biologists with no prior bioinformatics training [<a href="#ref-3">3</a>].

What should I report in the methods section about my enrichment figures?

Report the software and version used for enrichment analysis, the gene list definition and thresholds, the background gene set, the multiple testing correction method, and the criteria for selecting terms for display. This information allows readers to reproduce your analysis.

Why do different enrichment tools give me different results?

Different tools use different statistical methods, different gene set databases, and different background sets. Research shows that there is no agreement on standards and processes for GO enrichment analysis, and combining different methods can avoid the limitations of each one [<a href="#ref-2">2</a>]. If results differ substantially, investigate the source of the discrepancy.

How does sample size affect my enrichment visualization?

Small sample sizes reduce the power of differential expression analysis, which in turn affects enrichment results. Research on bulk RNA-seq found that differential expression and enrichment analysis results from underpowered experiments are unlikely to replicate well [<a href="#ref-19">19</a>]. If your sample size is small, interpret enrichment results cautiously and consider validating findings with additional experiments.

Related Bioinformatics Guides

• RNA-Seq Visualization: Volcano Plots, Heatmaps, and PCA
• How to Interpret Gene Set Enrichment Analysis Results
• Pathway Enrichment Analysis in R: Tools and Visualization for Omics Interpretation
• RNA-Seq Data Analysis in Galaxy: A User-Friendly Platform
• RNA-Seq Data Analysis Workflow: From Raw Reads to Insights

Related Clinical & Scientific Guides

• A Practical Guide to Detecting Antimicrobial Resistance Genes in Shotgun Metagenomic Data
• Computational Immunology: Modeling the Immune System
• How to Set Hard Filters for Germline Variant Calling: A Practical Guide to GATK Best Practices

References and Further Reading

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