Volcano Plot Generator
Paste log2 fold change and p-values to plot significance against effect size, threshold on the false discovery rate, label the top genes, and download a publication-ready SVG.
Drag & drop a file or
CSV, TSV, Excel (.xlsx/.xls) - max 5000 rows
Always labelled, whatever their rank.
Two Axes That Must Be Read Together
The volcano plot exists because neither effect size nor statistical significance means much on its own. A gene with a huge fold change but a weak p-value is often a low-count artefact, and a gene with a tiny fold change but an extreme p-value may be real yet biologically trivial. Plotting log2 fold change on the horizontal axis against the negative log10 of the p-value on the vertical axis puts both judgments in a single picture.
The negative log transform of the p-value is the trick that gives the plot its shape: it stretches the vanishingly small significant values, which would otherwise pile up against zero, into a tall spray while the non-significant majority stays low. The result is a dense unchanged cloud at the base and two significant arms rising to the left and right, the down- and up-regulated genes.
Correct thresholding is what separates a defensible figure from a misleading one. Because a differential expression experiment tests thousands of genes at once, the vertical threshold should be placed on the false discovery rate, not the raw p-value; this tool applies the Benjamini-Hochberg adjustment for you. Pair it with the false discovery rate calculator to see the adjusted values directly and the log2 fold change calculator to build the horizontal axis from raw counts.
A volcano plot is the headline figure of a differential expression study, but it is the end of a pipeline that starts with normalization and a proper count model. The RNA-seq normalization calculator handles the first step, and for the complete analysis from raw counts to an annotated, corrected gene list and figure, the bioinformatics analysis service delivers reproducible results.
A Worked Example: Why Both Axes Matter
A gene is called differentially expressed only when it clears both thresholds at once: an absolute log2 fold change of at least 1 and a Benjamini-Hochberg false discovery rate of 0.05 or below. The six genes below, classified exactly as this tool does, show why a single axis is never enough.
| Gene | log2 FC | p-value | FDR (BH) | Call |
|---|---|---|---|---|
| EGFR | +3.1 | 1e-6 | 6e-6 | up |
| PTEN | −2.2 | 8e-5 | 2e-4 | down |
| CDK4 | +1.1 | 0.03 | 0.045 | up |
| ACTB | +0.2 | 1e-4 | 2e-4 | not significanthighly significant, but the effect is below 1 |
| MYC | +2.5 | 0.3 | 0.36 | not significantlarge effect, but not significant |
| GAPDH | +0.05 | 0.9 | 0.9 | not significantneither criterion met |
The two instructive rows are ACTB and MYC. ACTB has an almost impossibly small p-value yet an effect of only 0.2, so it sits high but dead center and is not called: a change can be statistically certain and biologically trivial. MYC has a substantial 2.5 effect but a p-value of 0.3, so it sits far right but low and is also not called: a big number you cannot trust is not a finding. Only genes in the upper corners, such as EGFR and PTEN, clear both. CDK4 barely does, with an adjusted p-value of 0.045 just under the line.
Common Mistakes When Making a Volcano Plot
- Thresholding on the raw p-value. A differential-expression experiment tests thousands of genes, so raw p-values are riddled with false positives. Threshold on the false discovery rate; this tool applies Benjamini-Hochberg when you enable that option.
- Judging genes on fold change alone. A large log2 fold change on a noisy, low-count gene is often not significant. The whole point of the plot is to require height as well as width, so never rank by the horizontal axis by itself.
- Judging genes on significance alone. The mirror error: a microscopic p-value on a gene that barely moved, like a housekeeping gene, is real but meaningless. Require a minimum effect size too.
- Plotting the p-value instead of its negative log. The vertical axis must be −log10 of the p-value; plotting the raw p-value crushes every significant gene against zero and destroys the shape. This tool takes the transform for you.
- Feeding raw counts instead of test results. A volcano plot needs a per-gene log2 fold change and p-value from a model such as DESeq2, not raw counts. Run the differential-expression test first, then plot its output.
How to Use This Generator
Paste your results
One row per gene with a label, the log2 fold change, and the p-value.
Set thresholds
Choose fold-change and significance cutoffs, and threshold on the FDR-adjusted value for genome-wide data.
Read the plot
Up-regulated genes are red on the right, down-regulated blue on the left, top genes labelled.
Export
Download a publication-ready SVG or export the classified table as a CSV.
Next step
Want the full differential-expression analysis behind it?
Fold-change and false-discovery thresholds, gene-level statistics, and publication-ready figures, handled by a PhD statistician.
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Most projects deliver in under 2 weeks. We confirm an exact date in your quote.
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Frequently Asked Questions
What is a volcano plot?
A volcano plot is a scatter plot used in differential expression analysis that shows statistical significance against effect size for every gene at once. The horizontal axis is the log2 fold change and the vertical axis is the negative log10 of the p-value, so the most interesting genes, those that are both strongly changed and highly significant, sit in the upper left and upper right corners. It combines the two things you must consider together: how big a change is and how confident you are that it is real.
What does a volcano plot show?
It shows statistical significance against effect size for every gene in one view. The horizontal position is the log2 fold change, so how big the change is and in which direction, and the vertical position is the negative log10 of the p-value, so how confident you are that the change is real. Read together, the plot reveals which genes are both strongly changed and reliably significant, how many move up versus down, and how the bulk of unchanged genes compares to the handful of standout hits.
What data do you need for a volcano plot?
For each gene you need two numbers: a log2 fold change for the effect size and a p-value for significance, ideally with an adjusted p-value for false discovery rate control. A gene label makes the plot readable. This is exactly the output table of a differential-expression test from DESeq2, edgeR, or limma, so you do not feed raw counts into a volcano plot; you feed the per-gene statistics those tools produce. Paste or import a label, log2 fold change, and p-value column and the plot is drawn for you.
How to create a volcano plot?
Compute a log2 fold change and a p-value for every gene, transform the p-value to its negative log10 for the vertical axis, and plot that against the log2 fold change. Draw a vertical threshold line at your fold-change cutoff and a horizontal line at your significance cutoff, then color the genes that clear both. This generator does all of that: enter the three columns, set the thresholds, choose whether to threshold on the raw or Benjamini-Hochberg adjusted value, and export the figure.
How should a volcano plot look?
A healthy volcano plot has a dense cloud of unchanged, non-significant genes sitting low and centered near a log2 fold change of zero, with two arms of significant genes rising up and out to the left and right. The arms are roughly symmetric unless the biology is genuinely one-sided. A cloud that is shifted off center, or badly lopsided without a biological reason, often points to a normalization problem upstream rather than a real result.
How do you read a volcano plot?
Points far to the right are strongly up-regulated and points far to the left are strongly down-regulated, while points high on the vertical axis are the most statistically significant. Genes that clear both a fold-change threshold and a significance threshold are the differentially expressed hits, usually colored to stand out. Points near the bottom center are unchanged and non-significant. Dashed lines mark the thresholds so you can see which genes pass.
Should I threshold on the p-value or the adjusted p-value?
Use the adjusted p-value, which controls the false discovery rate, because a differential expression experiment tests thousands of genes and raw p-values include many false positives. This generator can threshold on the Benjamini-Hochberg adjusted value rather than the raw p-value, which is the correct choice for genome-wide analysis. Combining an FDR threshold with a fold-change threshold is standard practice.
What thresholds should I use for a volcano plot?
A common default is an absolute log2 fold change of at least 1, meaning a twofold change, together with a false discovery rate below 0.05. These are conventions, not rules: a study of subtle regulation may lower the fold-change cutoff, while a very large study may tighten the FDR. The right thresholds depend on the biology and the tolerance for false positives, and they should be decided before looking at the results.
Related Analysis Tools
To compute the effect-size axis from counts, use the log2 fold change calculator, and to see the adjusted significance values the false discovery rate calculator applies the correction. To visualize the significant genes as a clustered matrix, the heatmap generator reorders by similarity. For the full differential-expression analysis, the bioinformatics analysis service delivers publication-ready results.
Reviewed by
Dr. Sarah Mitchell
Dr. Sarah Mitchell holds a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health and has over 15 years of experience in systematic review methodology and meta-analysis. She has authored or co-authored 40+ peer-reviewed publications in journals including the Journal of Clinical Epidemiology, BMC Medical Research Methodology, and Research Synthesis Methods. A former Cochrane Review Group statistician and current editorial board member of Systematic Reviews, Dr. Mitchell has supervised 200+ evidence synthesis projects across clinical medicine, public health, and social sciences. She reviews all Research Gold tools to ensure statistical accuracy and compliance with Cochrane Handbook and PRISMA 2020 standards.
The Tool Is Free. The Full Analysis of Your Biological Data? We Handle That.
Our PhD statisticians run the complete pipeline: differential expression with multiple-testing correction, survival modelling, dimensionality reduction, and publication-ready figures with a reproducible methods section. Constant pricing, most projects delivered in under two weeks.
Our promise: Free pipeline re-run and figure revisions if reviewers push back.