EnhancedVolcano for RNA-seq: How to Create Publication-Ready Volcano Plots with Custom Labels and Thresholds

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

Key Takeaways

• EnhancedVolcano is an R package that extends base ggplot2 for generating publication-ready volcano plots from RNA-seq differential expression results, offering improved gene labeling and customizable thresholds.
• The package requires a data frame with gene identifiers, log2 fold changes, and adjusted p-values, typically derived from tools like DESeq2, edgeR, or limma, and handles missing values by excluding affected genes.
• Threshold selection is critical, with pCutoff (adjusted p-value) and FCcutoff (log2 fold change) parameters determining which genes are highlighted; standard choices include p < 0.05 and |log2FC| > 1, but these should be tailored to study goals (e.g., stringent for confirmation, relaxed for discovery).
• Gene labeling strategies are flexible, allowing selection by significance, rank, or custom criteria via the selectLab parameter, with repel = TRUE essential for managing label overlap in dense plots.
• Aesthetic customization, including color schemes (consider colorblind-safe palettes), point size, transparency, and axis limits, is crucial for clarity and adherence to journal requirements.
• Reproducibility is paramount; document R and package versions (sessionInfo()), preserve analysis scripts, and clearly report all parameters used in the EnhancedVolcano function call within the manuscript's methods section and figure legends.

Volcano plots are a standard visualization in differential expression analysis, displaying statistical significance against magnitude of change for thousands of genes simultaneously. EnhancedVolcano is an R package that extends the base ggplot2 volcano plot with improved labeling, customizable thresholds, and publication-oriented aesthetics. This article provides a practical workflow for generating volcano plots that meet journal requirements, with specific attention to labeling strategies, threshold selection, and reproducible figure generation.

The Role of Volcano Plots in RNA-seq Reporting

Differential expression analysis produces a table of results containing log2 fold changes and adjusted p-values for each gene. A volcano plot projects these two dimensions onto a single scatter plot, where the x-axis represents the magnitude of expression change and the y-axis represents statistical significance. The resulting visualization allows rapid identification of genes that are both highly changed and statistically robust.

The utility of volcano plots extends beyond simple visualization. Reviewers and readers use these figures to assess the overall distribution of expression changes, evaluate the effectiveness of experimental contrasts, and identify candidate genes for follow-up validation. A well-constructed volcano plot communicates the biological signal in a dataset more efficiently than a table of thousands of genes.

RNA-seq analysis workflows typically include quality control, alignment, quantification, and differential expression testing. The Galaxy Training Network provides accessible tutorials covering these steps, while Bioconductor hosts the primary R packages used throughout the analysis pipeline. Understanding where volcano plot generation fits within the broader workflow helps researchers allocate appropriate attention to this final visualization step.

Understanding EnhancedVolcano Package Architecture

EnhancedVolcano is distributed through Bioconductor, the primary repository for open-source bioinformatics software in R. The package accepts a differential expression results table and produces a ggplot2-based volcano plot with several enhancements over base plotting functions.

The core function requires a data frame containing gene identifiers, log2 fold changes, and adjusted p-values. These inputs typically come from DESeq2, edgeR, limma, or other differential expression tools. The package handles the transformation of p-values to negative log10 scale internally, allowing users to specify thresholds on the original scale.

Key parameters control the appearance and content of the plot. The pCutoff parameter sets the adjusted p-value threshold, while FCcutoff sets the fold change threshold. Genes exceeding both thresholds are highlighted in the plot. The lab parameter accepts a character vector of gene labels, and labSize controls label font size. Additional parameters manage point transparency, color schemes, and axis limits.

The package also supports drawing vertical and horizontal lines at threshold boundaries, adding a legend that identifies significant and non-significant genes, and adjusting the plot title and axis labels. These features make EnhancedVolcano suitable for producing figures that conform to journal style guidelines without requiring extensive manual post-processing.

Preparing Differential Expression Results for EnhancedVolcano

Before generating a volcano plot, researchers must complete the upstream analysis steps. The NCBI provides access to sequence read archives and reference genomes, while EMBL-EBI Training offers structured learning pathways for bioinformatics analysis. The quality of the volcano plot depends entirely on the quality of the differential expression results.

Required Data Structure

EnhancedVolcano expects a data frame with specific columns. The first column should contain gene identifiers, typically Ensembl gene IDs, Entrez IDs, or gene symbols. The second column should contain log2 fold changes, and the third column should contain adjusted p-values. The exact column names can be specified using the lab, x, and y arguments if they differ from the defaults.

Most differential expression tools produce output tables that require minimal reformatting. DESeq2 results can be converted to a data frame using the as.data.frame() function, and the row names containing gene identifiers can be moved to a dedicated column. edgeR and limma outputs follow similar patterns.

Handling Missing Values

Differential expression results often contain missing values. Genes with extremely low expression may have undefined fold changes, and some statistical tests produce NA p-values for genes filtered during analysis. EnhancedVolcano handles missing values by excluding those genes from the plot, but researchers should verify that the number of excluded genes is reasonable and document this filtering in methods sections.

Gene Identifier Consistency

The gene labels displayed on the volcano plot should match the identifiers used throughout the manuscript. If the differential expression results use Ensembl IDs but the manuscript discusses gene symbols, researchers should map identifiers before generating the plot. This mapping can be performed using Bioconductor annotation packages or online resources such as those available through NCBI.

Setting P-Value and Fold Change Thresholds

Threshold selection is one of the most consequential decisions in volcano plot generation. The thresholds determine which genes are highlighted as significant and therefore influence the biological interpretation of the figure.

Adjusted P-Value Thresholds

The standard threshold for adjusted p-values in differential expression analysis is 0.05, though some studies use more stringent thresholds such as 0.01 or 0.001. The choice depends on the number of tests performed, the expected effect sizes, and the tolerance for false positives in downstream validation.

EnhancedVolcano uses the pCutoff parameter to define the significance boundary. Genes with adjusted p-values below this threshold are considered statistically significant. The package automatically transforms this value to the negative log10 scale for display on the y-axis.

Researchers should consider whether to use the default adjusted p-value or a custom threshold. The Benjamini-Hochberg procedure is the most common method for controlling the false discovery rate in RNA-seq studies. The adjusted p-values produced by this method account for multiple testing and are appropriate for volcano plot thresholds.

Fold Change Thresholds

Fold change thresholds are more variable across studies. A common default is a log2 fold change of 1, corresponding to a two-fold change in expression. Some studies use more stringent thresholds such as log2 fold change of 2, while exploratory analyses may use less stringent thresholds.

The FCcutoff parameter in EnhancedVolcano sets the fold change boundary. Genes with absolute log2 fold changes above this threshold are considered biologically meaningful. The choice of fold change threshold should be justified based on the expected effect sizes in the experimental system and the goals of the analysis.

Combined Threshold Interpretation

Genes that pass both the p-value and fold change thresholds are highlighted in the volcano plot. This combined criterion ensures that highlighted genes are both statistically significant and biologically meaningful. The number of genes passing both thresholds should be reported in the figure legend or methods section.

Researchers should examine the distribution of genes relative to the thresholds before finalizing the plot. If very few genes pass the combined thresholds, the thresholds may be too stringent for the dataset. Conversely, if thousands of genes pass the thresholds, the thresholds may be too lenient to provide meaningful biological separation.

At a Glance: Threshold Selection and Plot Configuration

The following table summarizes common threshold configurations and their appropriate use cases in RNA-seq differential expression analysis.

Labeling Top Genes in Volcano Plots

Gene labeling is a critical feature that distinguishes EnhancedVolcano from base plotting functions. The package provides several strategies for labeling genes, each with specific advantages and limitations.

Labeling by Significance

The simplest labeling strategy highlights genes that pass both thresholds. The lab parameter accepts a character vector of gene names, and the selectLab parameter can be used to specify which genes receive labels. By default, EnhancedVolcano labels all genes that pass both thresholds, which can produce cluttered plots when hundreds of genes are significant.

Labeling by Rank

Researchers often want to label the top genes by significance or fold change. The selectLab parameter accepts a character vector of gene names to label. This approach allows researchers to choose specific genes of biological interest, such as known markers or genes identified in previous studies.

Labeling with Custom Criteria

EnhancedVolcano supports labeling genes based on custom criteria. Researchers can create a logical vector indicating which genes should be labeled and pass this to the selectLab parameter. This approach provides maximum flexibility for highlighting genes of interest while excluding others.

Managing Label Overlap

Label overlap is a common problem when many genes are labeled. EnhancedVolcano includes a maxoverlaps parameter that controls the maximum number of overlapping labels allowed. Increasing this value allows more labels to be displayed but may result in overlapping text. Decreasing the value reduces clutter but may omit some genes from the plot.

The repel parameter controls whether labels are repelled from each other to reduce overlap. When set to TRUE, labels are positioned to minimize overlap using the ggrepel package. This feature is essential for publication-quality figures with multiple labeled genes.

Customizing Plot Aesthetics for Publication

Publication requirements vary across journals, but several aesthetic considerations apply broadly to volcano plots. EnhancedVolcano provides parameters for controlling colors, point sizes, transparency, and axis limits.

Color Schemes

The default color scheme uses red for genes passing both thresholds, blue for genes passing only the p-value threshold, green for genes passing only the fold change threshold, and grey for non-significant genes. This color scheme effectively communicates the significance status of each gene.

Some journals require colorblind-safe palettes. EnhancedVolcano allows custom color specification through the col parameter, which accepts a vector of colors for the different gene categories. Researchers should verify that their chosen colors are distinguishable for readers with color vision deficiencies.

Point Size and Transparency

The pointSize parameter controls the size of individual points in the plot. Larger point sizes improve visibility but may obscure overlapping points. The transparency parameter controls point opacity, allowing researchers to visualize the density of points in crowded regions of the plot.

For datasets with many genes, transparency is essential for revealing the underlying distribution. A transparency value between 0.3 and 0.6 typically provides good visualization of point density while maintaining visibility of individual points.

Axis Limits

The xlim and ylim parameters control the axis limits of the plot. Setting appropriate limits ensures that the plot focuses on the region of biological interest while excluding extreme outliers that compress the main distribution.

Researchers should examine the distribution of log2 fold changes and p-values before setting axis limits. Extreme values can dominate the plot and obscure the majority of genes. Trimming the axes to the central 95 percent of the distribution often produces a more informative figure.

Plot Dimensions

The width and height parameters control the output dimensions of the plot. Publication requirements typically specify figure dimensions in inches or centimeters. Researchers should generate plots at the required dimensions to avoid resolution issues during manuscript submission.

Practical Workflow for Generating EnhancedVolcano Plots

The following workflow outlines the steps for generating a publication-ready volcano plot using EnhancedVolcano. This workflow assumes that differential expression analysis has been completed and results are available in R.

Step 1: Install and Load Required Packages

EnhancedVolcano is available through Bioconductor. Installation requires the BiocManager package, which can be installed from CRAN. After installation, load EnhancedVolcano and the ggplot2 package for additional customization.

Step 2: Prepare the Results Data Frame

Load the differential expression results into R and ensure the data frame contains the required columns. Verify that gene identifiers are unique and that no duplicate rows exist. Check for missing values and decide how to handle them.

Step 3: Set Thresholds

Determine the adjusted p-value threshold and fold change threshold based on the study design and analysis goals. Document the rationale for threshold selection in the methods section.

Step 4: Generate the Initial Plot

Create an initial volcano plot using default parameters. Examine the plot to assess the distribution of genes, the number of significant genes, and the overall appearance.

Step 5: Adjust Labeling

Select genes for labeling based on biological interest or statistical significance. Adjust the selectLab parameter to include the desired genes. Increase the maxoverlaps parameter if labels are being omitted.

Step 6: Customize Aesthetics

Adjust colors, point sizes, transparency, and axis limits to produce a clear and informative figure. Verify that the plot is legible at the dimensions required for publication.

Step 7: Save the Plot

Save the plot in a vector format such as PDF or SVG for maximum quality. Also save a high-resolution raster version for applications that require raster images.

Reproducibility and Documentation

Reproducibility is a core principle of bioinformatics analysis. The nf-core documentation emphasizes the importance of version control and containerization for reproducible workflows. Volcano plot generation should follow the same principles.

Version Documentation

Record the versions of R, EnhancedVolcano, and all dependent packages used to generate the plot. The sessionInfo() function in R provides a complete record of the computing environment. Include this information in the methods section or supplementary materials.

Script Preservation

Save the R script used to generate the volcano plot. The script should be well-commented and organized so that other researchers can reproduce the figure from the raw differential expression results.

Parameter Documentation

Document all parameters used in the EnhancedVolcano function call, including thresholds, labeling choices, and aesthetic settings. This documentation ensures that the figure can be regenerated with identical settings.

Common Failure Patterns and Troubleshooting

Several common issues arise when generating volcano plots with EnhancedVolcano. Understanding these failure patterns helps researchers diagnose and resolve problems efficiently.

Missing Gene Labels

When gene labels do not appear on the plot, the selectLab parameter may reference gene names that do not match the identifiers in the data frame. Verify that the gene names in selectLab exactly match the values in the label column, including case and formatting.

Excessive Label Overlap

When labels overlap excessively, the maxoverlaps parameter may be set too high. Reduce the value to allow fewer overlapping labels, or reduce the number of genes selected for labeling.

Points Missing from Plot

When points are missing from the plot, the data frame may contain missing values that are being excluded. Check for NA values in the log2 fold change and adjusted p-value columns. Also verify that the axis limits include the range of the data.

Threshold Lines Not Visible

When threshold lines do not appear, the drawConnectors parameter may be set incorrectly. Verify that the pCutoff and FCcutoff values are within the range of the data. Threshold lines may be obscured by dense point clouds.

Plot Rendering Errors

When the plot fails to render, the issue may be related to package conflicts or version incompatibilities. Verify that all required packages are installed and that the R version is compatible with EnhancedVolcano.

Interpreting Volcano Plots in Biological Context

Volcano plots provide a statistical summary of differential expression, but biological interpretation requires additional context. The European Bioinformatics Institute offers training materials on functional interpretation of omics data, which complement the statistical visualization provided by volcano plots.

Assessing Effect Size Distribution

The distribution of log2 fold changes across all genes provides information about the overall magnitude of expression changes in the experiment. A narrow distribution centered near zero suggests modest global changes, while a wide distribution indicates substantial transcriptional remodeling.

Evaluating Significance Distribution

The distribution of adjusted p-values reveals the statistical power of the experiment. A large number of genes with very small p-values suggests adequate sample sizes and consistent biological effects. A flat distribution of p-values may indicate technical issues or insufficient statistical power.

Identifying Outlier Genes

Genes with extreme fold changes or very small p-values warrant individual examination. These genes may represent true biological signals or technical artifacts. Manual inspection of read counts and alignment quality for outlier genes is recommended before highlighting them in publications.

Comparing Across Conditions

Volcano plots from different experimental contrasts can be compared to identify shared and unique patterns of differential expression. This comparison is most informative when the same thresholds and labeling criteria are used across plots.

Limitations of Volcano Plot Visualization

Volcano plots have inherent limitations that researchers should acknowledge when using them for publication. Understanding these limitations helps prevent overinterpretation of the visualization.

Loss of Per-Gene Variability Information

Volcano plots display only the summary statistics for each gene, not the underlying variability across biological replicates. Genes with identical fold changes and p-values may have very different expression levels and variability patterns.

Threshold Arbitrariness

The thresholds used to define significance are somewhat arbitrary and may not reflect biological importance. A gene with a log2 fold change of 0.9 and an adjusted p-value of 0.04 may be biologically meaningful even though it does not pass standard thresholds.

Visual Overemphasis of Extreme Values

Genes with extreme fold changes or very small p-values dominate the visual appearance of volcano plots. These genes may not be the most biologically important, and their visual prominence can distract from more subtle but meaningful changes.

Inability to Display Multiple Dimensions

Volcano plots display only two dimensions of the data. Additional information such as gene expression level, functional category, or genomic location cannot be displayed simultaneously without using additional visual encodings.

Quality Control Checks for Volcano Plot Generation

Before finalizing a volcano plot for publication, researchers should perform several quality control checks to ensure the figure accurately represents the underlying data.

Verify Gene Counts

The total number of points in the plot should match the number of genes in the differential expression results after filtering. Count the points in the plot and compare with the expected number.

Confirm Threshold Boundaries

Verify that the threshold lines are positioned at the specified values. Check that genes on either side of the threshold lines have the expected significance status.

Validate Label Accuracy

Confirm that labeled genes correspond to the intended identifiers. Check for spelling errors or identifier mismatches that could mislabel genes in the figure.

Assess Readability at Publication Size

Generate the plot at the dimensions required for publication and verify that all elements are legible. Labels should be readable, points should be distinguishable, and threshold lines should be visible.

Integrating Volcano Plots into Broader Analysis Workflows

Volcano plots are typically one component of a larger analysis workflow. The Carpentries offers foundational training in data analysis and visualization that supports reproducible research practices. Integrating volcano plot generation into a documented workflow ensures consistency across analyses.

Relationship to MA Plots

MA plots display the relationship between mean expression and log2 fold change. While volcano plots focus on significance and effect size, MA plots reveal expression-level dependent patterns. Generating both visualizations provides complementary perspectives on the data.

Relationship to Heatmaps

Heatmaps display expression values for selected genes across samples. Volcano plots identify candidate genes, while heatmaps show the expression patterns of those genes across experimental conditions. Combining these visualizations provides a more complete picture of the data.

Relationship to Pathway Analysis

Pathway analysis tools identify functional categories enriched among differentially expressed genes. Volcano plots can highlight genes belonging to specific pathways by using custom labeling or coloring. This integration connects statistical significance to biological function.

Advanced Customization Options

EnhancedVolcano provides several advanced options for researchers who need additional control over plot appearance.

Custom Point Shapes

The shape parameter allows different point shapes for different gene categories. This feature is useful for distinguishing genes with positive and negative fold changes or for highlighting specific gene sets.

Custom Legends

The legendLabels parameter controls the text displayed in the legend. Researchers can customize legend labels to match the terminology used in their manuscript.

Multiple Contrasts

EnhancedVolcano can generate plots for multiple contrasts in a single call using the list parameter. This feature is useful for comparing results across experimental conditions.

Faceted Plots

The facet parameter allows splitting the plot by a categorical variable. This feature is useful for displaying results from multiple comparisons in a single figure.

Reporting Volcano Plot Parameters in Publications

Transparent reporting of volcano plot parameters is essential for reproducibility. The methods section should include the specific parameters used to generate the figure.

Required Information

The methods section should specify the adjusted p-value threshold, the fold change threshold, the labeling criteria, and the version of EnhancedVolcano used. This information allows readers to understand the criteria used to define significance in the figure.

Supplementary Materials

The R script used to generate the plot should be included in supplementary materials. The script should be annotated to explain the purpose of each step and the rationale for parameter choices.

Figure Legends

The figure legend should describe the axes, the significance thresholds, and the labeling criteria. The legend should also state the total number of genes displayed and the number of genes passing each threshold.

Professional Escalation Criteria

Some situations require consultation with bioinformatics specialists or statisticians before finalizing volcano plots for publication.

Unexpected Threshold Results

When the number of genes passing the combined thresholds is unexpectedly high or low, consult with a statistician to evaluate whether the thresholds are appropriate for the dataset.

Discrepancies Between Tools

When different differential expression tools produce substantially different results, consult with a bioinformatics specialist to identify the source of the discrepancy.

Complex Experimental Designs

When the experimental design includes multiple factors, batch effects, or repeated measures, consult with a statistician to ensure that the differential expression analysis and volcano plot appropriately account for the design.

Publication-Specific Requirements

When a journal has specific requirements for figure formatting or statistical reporting, consult the journal guidelines and contact the editorial office if clarification is needed.

Building a Decision Framework for Volcano Plot Threshold Selection

Threshold selection in volcano plot generation often receives insufficient attention relative to its impact on the biological conclusions drawn from the figure. Researchers frequently default to conventional cutoffs without systematically evaluating whether those cutoffs serve the specific goals of their study. This section provides a structured decision framework that connects experimental objectives to threshold choices, establishes a record system for documenting those choices, and offers troubleshooting methods for common threshold-related problems.

Connecting Experimental Objectives to Threshold Choices

The first step in threshold selection is articulating the primary purpose of the volcano plot. Different research goals require different threshold configurations, and applying a one-size-fits-all approach can undermine the figure's utility.

Hypothesis-Confirming Studies

When the volcano plot is intended to confirm previously identified candidate genes or validate findings from prior studies, stringent thresholds are appropriate. The goal is to minimize false positives and present a conservative set of differentially expressed genes. For these studies, an adjusted p-value threshold of 0.01 or lower combined with a log2 fold change threshold of 1.5 or 2 provides strong evidence for the claims being made. The Bioconductor documentation for differential expression packages emphasizes that conservative thresholds reduce the burden of downstream validation experiments.

Hypothesis-Generating Studies

Exploratory studies seeking to identify novel candidate genes for future investigation benefit from more permissive thresholds. The goal is to capture a broader set of potential signals while accepting a higher false positive rate. An adjusted p-value threshold of 0.05 with a log2 fold change threshold of 0.5 or 1 allows the visualization to reveal patterns that might be missed with stringent cutoffs. The Galaxy Training Network tutorials on differential expression analysis note that exploratory analyses often prioritize sensitivity over specificity.

Meta-Analysis and Cross-Study Comparison

When comparing results across multiple studies or datasets, thresholds must be consistent across all comparisons. Inconsistent thresholds make it impossible to determine whether differences between studies reflect biological variation or analytical choices. Researchers should establish a threshold protocol before generating any plots and apply it uniformly. The nf-core documentation emphasizes that consistent parameterization is essential for reproducible cross-study comparisons.

Clinical or Translational Applications

Studies with potential clinical implications require careful consideration of both statistical and biological significance. The thresholds should reflect the magnitude of change that would be clinically meaningful, which may differ substantially from default values. For example, a study examining biomarkers for disease diagnosis might require larger fold changes than a basic mechanistic study. The pan-cancer analysis of peritoneal metastasis published in npj Precision Oncology demonstrates how threshold choices directly influence which genomic alterations are highlighted as clinically relevant drivers.

Establishing a Threshold Decision Record

Documenting threshold decisions is essential for reproducibility and for defending analytical choices during peer review. A structured record system ensures that the rationale behind each threshold is captured and available for reference.

Threshold Decision Log

Create a table that records the following information for each volcano plot generated:

This log should be maintained alongside the analysis scripts and updated whenever thresholds are revised. The Carpentries lessons on reproducible research recommend maintaining such documentation as part of standard data management practices.

Threshold Sensitivity Assessment

Before finalizing thresholds, conduct a sensitivity assessment to understand how the number of significant genes changes across a range of threshold values. This assessment provides context for the chosen thresholds and helps justify the final selection.

Generate volcano plots or summary statistics using the following threshold combinations:

1. Adjusted p-value of 0.05 with log2 fold change of 0.5
2. Adjusted p-value of 0.05 with log2 fold change of 1
3. Adjusted p-value of 0.05 with log2 fold change of 2
4. Adjusted p-value of 0.01 with log2 fold change of 1
5. Adjusted p-value of 0.01 with log2 fold change of 2

Record the number of genes passing each combination. This sensitivity table demonstrates that the chosen thresholds are not arbitrary but were selected after evaluating the trade-offs between sensitivity and specificity. The approach mirrors the bootstrap-based accuracy assessment methods described in the psychological network estimation tutorial published in Behavior Research Methods, which emphasizes evaluating how analytical choices affect the stability of conclusions.

Threshold Justification Statement

Draft a brief statement that explains the threshold selection in plain language. This statement should be included in the methods section of the manuscript and should address the following points:

• The biological rationale for the fold change threshold
• The statistical rationale for the adjusted p-value threshold
• How the thresholds relate to the study goals
• What proportion of genes passed the combined thresholds

Having this statement prepared in advance simplifies manuscript writing and ensures that the rationale is documented while the analysis decisions are still fresh.

Troubleshooting Threshold-Related Problems

Several common problems arise specifically from threshold selection. Recognizing these patterns helps researchers diagnose issues quickly and adjust their approach.

Too Few Significant Genes

When very few genes pass the combined thresholds, the volcano plot appears sparse and provides limited biological insight. This situation typically indicates that the thresholds are too stringent for the dataset.

Possible causes include:

• Small sample sizes that limit statistical power
• Subtle biological effects that produce modest fold changes
• High biological variability that inflates p-values
• Inappropriate threshold selection for the experimental system

The study on early-life stress effects on the habenula and insular cortex published in Neurobiology of Stress illustrates this challenge. The transcriptomic analysis revealed profound gene expression changes, but the authors needed to carefully select thresholds to capture the sex-specific patterns that were central to their findings. When too few genes pass the thresholds, consider whether the experimental design provides adequate power or whether the thresholds should be relaxed for exploratory purposes.

Too Many Significant Genes

When thousands of genes pass the combined thresholds, the volcano plot becomes cluttered and the highlighted genes lose their distinctiveness. This situation often occurs in experiments with large sample sizes or strong biological perturbations.

Possible responses include:

• Increasing the fold change threshold to focus on genes with larger effects
• Increasing the adjusted p-value threshold stringency
• Using additional filtering criteria such as minimum expression levels
• Considering whether the biological contrast is too broad

The ROR1-PI3K/AKT signaling study published in Cell Death and Disease demonstrates how multi-omics profiling can identify a focused set of genes driving drug resistance. The authors used integrated analysis to narrow their focus to a specific signaling node instead of presenting the full set of differentially expressed genes.

Inconsistent Results Across Biological Replicates

When the number of significant genes varies dramatically across replicate analyses, the thresholds may be sensitive to sampling variation. This instability undermines confidence in the identified gene sets.

The tutorial on estimating psychological networks published in Behavior Research Methods describes bootstrap methods for assessing the stability of analytical results. Similar approaches can be applied to differential expression analysis by resampling the data and examining how the number of significant genes changes across bootstrap iterations.

Threshold Lines Obscured by Data Points

When threshold lines are difficult to see because of dense point clouds, the visualization fails to communicate the threshold boundaries effectively. This problem is distinct from threshold selection itself but affects the interpretability of the figure.

Solutions include:

• Adjusting point transparency to reveal the threshold lines
• Using different line types or colors for threshold boundaries
• Zooming into the region of interest by adjusting axis limits
• Adding annotations that state the threshold values directly

Integrating Threshold Decisions with Labeling Strategies

The thresholds chosen for significance determination should inform the labeling strategy. Genes that pass the combined thresholds are natural candidates for labeling, but the number of labeled genes must be manageable for the figure to remain readable.

Labeling Priority Framework

When the number of significant genes exceeds what can be reasonably labeled, establish a priority framework for selecting which genes receive labels:

1. Genes with known biological relevance to the study question
2. Genes with the largest absolute fold changes
3. Genes with the smallest adjusted p-values
4. Genes that appear in multiple significant contrasts

This framework ensures that the most informative genes are labeled while preventing label clutter. The EnhancedVolcano selectLab parameter allows precise control over which genes are labeled, and the maxoverlaps parameter manages the trade-off between label completeness and readability.

Label Count Targets

For a standard publication figure, aim to label between 10 and 30 genes. This range provides sufficient information for readers to identify key candidates without overwhelming the figure. If more than 30 genes require labeling, consider whether the thresholds should be adjusted or whether the figure should be split into multiple panels.

The spatial transcriptomics study published in Nature Communications demonstrates how multimodal analysis can identify a focused set of genes for highlighting. The authors used single-cell RNA sequencing and spatial transcriptomics to identify regulatory T cells as key mediators, allowing them to focus their visualization on the most relevant genes.

Recording Threshold Decisions in Analysis Scripts

The R script used to generate volcano plots should include explicit documentation of threshold decisions. This documentation serves both reproducibility and peer review purposes.

Script Annotation Template

Include comments in the script that document:

• The rationale for the adjusted p-value threshold
• The rationale for the fold change threshold
• The number of genes passing each threshold
• The date and version of the analysis

Example annotation structure:

## Threshold selection for volcano plot
## Study goal: Identify genes with robust expression changes
## Adjusted p-value threshold: 0.05 (Benjamini-Hochberg corrected)
## Rationale: Standard FDR control for exploratory analysis
## Fold change threshold: log2 fold change of 1 (two-fold change)
## Rationale: Two-fold change represents biologically meaningful effect
## Expected number of significant genes: approximately 500

This annotation ensures that anyone reviewing the script understands the analytical decisions without needing to consult separate documentation.

Common Threshold Mistakes and Prevention

Several recurring mistakes undermine the validity of volcano plot thresholds. Recognizing these patterns helps researchers avoid them.

Using Unadjusted P-Values

Some researchers mistakenly use raw p-values instead of adjusted p-values for threshold determination. This error dramatically inflates the number of significant genes because it ignores the multiple testing problem inherent in genome-wide analyses. Always use adjusted p-values from the differential expression tool, which account for the number of tests performed.

Applying Thresholds Without Biological Justification

Thresholds selected without reference to the biological system or study goals may highlight genes that are statistically significant but biologically irrelevant. The fold change threshold should reflect the effect size that matters for the specific experimental context, not a universal default.

Inconsistent Thresholds Across Figures

When multiple volcano plots appear in the same manuscript, they should use consistent thresholds unless there is a specific reason for variation. Inconsistent thresholds make it difficult for readers to compare results across figures and may raise questions about analytical rigor.

Ignoring the Distribution of Results

Thresholds should be selected after examining the distribution of fold changes and p-values in the dataset. A threshold that works well for one dataset may be inappropriate for another with different distributional characteristics. The EMBL-EBI Training materials on functional genomics emphasize the importance of understanding data distributions before applying analytical cutoffs.

Professional Escalation for Threshold Decisions

Some threshold decisions require consultation with statistical experts or bioinformatics specialists.

When to Seek Statistical Consultation

Consult a statistician when:

• The number of significant genes is unexpectedly high or low relative to similar studies
• The experimental design involves complex factors that complicate threshold interpretation
• The study has regulatory or clinical implications that require rigorous statistical justification
• Reviewers have raised concerns about threshold appropriateness

When to Seek Bioinformatics Consultation

Consult a bioinformatics specialist when:

• Different differential expression tools produce substantially different gene sets at the same thresholds
• The results table contains anomalies such as unexpected missing values or extreme outliers
• The analysis pipeline requires integration of multiple data types that affect threshold interpretation

The protocol for deep proteomic profiling of formalin-fixed paraffin-embedded specimens published in STAR Protocols demonstrates how specialized expertise is often needed to navigate the analytical challenges of complex biomedical datasets. Similarly, threshold decisions in RNA-seq analysis may benefit from specialized consultation when the experimental context is unusual.

Building a Threshold Decision Checklist

The following checklist summarizes the key steps in the threshold decision framework:

1. Articulate the primary study goal for the volcano plot
2. Determine whether the analysis is hypothesis-confirming or hypothesis-generating
3. Select initial thresholds based on the study goal
4. Generate a sensitivity table showing gene counts across threshold combinations
5. Examine the distribution of fold changes and p-values in the dataset
6. Adjust thresholds based on the sensitivity assessment and data distribution
7. Document the final thresholds and their rationale in the decision log
8. Verify that the number of significant genes is appropriate for the study context
9. Select labeling priorities based on the significant gene set
10. Record all decisions in the analysis script and methods section

This checklist ensures that threshold selection is a deliberate, documented process instead of an arbitrary default. The resulting volcano plots will be more defensible in peer review and more informative for readers.

Frequently Asked Questions

What is the difference between p-value and adjusted p-value in volcano plots?

The p-value represents the probability of observing the data given that the null hypothesis is true. The adjusted p-value accounts for multiple testing by controlling the false discovery rate across all genes tested. Volcano plots should use adjusted p-values to avoid inflating the number of significant genes due to chance.

How do I choose the fold change threshold for my volcano plot?

The fold change threshold should reflect the biological effect size that is meaningful for your experimental system. A log2 fold change of 1 corresponds to a two-fold change and is a common default. More stringent thresholds may be appropriate when effect sizes are large, while less stringent thresholds may be needed for exploratory analyses.

Why are some genes not labeled even though they pass the significance thresholds?

EnhancedVolcano limits the number of labels to prevent overlap. The maxoverlaps parameter controls this limit. Increasing this value allows more labels to be displayed, while reducing the number of genes selected for labeling can also resolve the issue.

Can I use EnhancedVolcano with results from any differential expression tool?

EnhancedVolcano accepts any data frame containing gene identifiers, log2 fold changes, and adjusted p-values. Results from DESeq2, edgeR, limma, and other tools can be used after converting the results to a data frame with the required columns.

How do I change the colors of points in my volcano plot?

The col parameter accepts a vector of colors for the different gene categories. The order of colors corresponds to genes passing both thresholds, genes passing only the p-value threshold, genes passing only the fold change threshold, and non-significant genes.

What file format should I use for publication figures?

Vector formats such as PDF or SVG are preferred for publication because they scale without loss of quality. High-resolution raster formats such as TIFF or PNG at 300 DPI are acceptable for journals that require raster images.

How do I add custom labels to specific genes in my volcano plot?

The selectLab parameter accepts a character vector of gene names to label. Only genes included in this vector will receive labels, regardless of whether they pass the significance thresholds.

Can I generate volcano plots for multiple comparisons in a single script?

Yes, EnhancedVolcano can generate plots for multiple contrasts by calling the function multiple times with different results tables. The list parameter also allows generating multiple plots in a single call.

Related Bioinformatics Guides

• RNA-Seq Visualization: Volcano Plots, Heatmaps, and PCA
• RNA-Seq vs qPCR: Validation and Comparison
• RNA-Seq Batch Effect Detection and Correction
• RNA-Seq vs DNA-Seq: Key Differences and Applications
• RNA-Seq Alignment: Choosing the Right Tool and Parameters

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

• NCBI Data Resources. National Center for Biotechnology Information.
• EMBL-EBI Training. European Bioinformatics Institute.
• Bioconductor. Bioconductor Project.
• Galaxy Training Network. Galaxy Project.
• nf-core Documentation. nf-core.
• The Carpentries Lessons. The Carpentries.
• Protocol for deep proteomic profiling of formalin-fixed paraffin-embedded specimens using a spectral library-free approach.. 2023.
• Tracing immune cells around biomaterials with spatial anchors during large-scale wound regeneration.. 2023.
• Pan-cancer clinicopathological and genomic characteristics of peritoneal metastasis.. 2025.
• Exposure to early-life stress uncovers shared biological signatures underlying vulnerability in the habenula and insular cortex of male and female adult rats.. 2025.
• ROR1-PI3K/AKT signaling drives adaptive resistance to cell cycle blockade in TP53 mutated ovarian cancer.. 2026.
• A Tutorial on Movable Antennas for Wireless Networks. IEEE Communications Surveys and Tutorials, 2025.
• Pinching-Antenna Systems (PASS): A Tutorial. IEEE Transactions on Communications, 2025.
• Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 2016.
• A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs. IEEE Communications Surveys and Tutorials, 2024.
• Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial. IEEE Transactions on Communications, 2020.
• Semantics-Empowered Communications: A Tutorial-Cum-Survey. IEEE Communications Surveys and Tutorials, 2024.
• A tutorial on open-source large language models for behavioral science. Behavior Research Methods, 2024.

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