Volcano Plot Generator
Turn differential expression data into a publication-ready figure
Describe your RNA-seq, proteomics, or differential expression results and AI draws a clean volcano plot — significance thresholds, color-coded up- and down-regulated genes, and labeled top hits — ready for manuscripts and talks.
Volcano plot examples
Click any example to load its prompt, or use it as a starting point for your own volcano plot.
What does this volcano plot generator do?
It turns a description of your differential expression results into a clean, labeled volcano plot — the scatter plot that puts log2 fold change on the x-axis and statistical significance (−log10 p-value) on the y-axis. You describe your comparison, your thresholds, and the genes you want highlighted, and the AI draws the points, dashed cutoff lines, and labels so significantly up- and down-regulated features stand out at a glance, without plotting code or manual annotation.
Why use a volcano plot generator
- Volcano plots are the standard way to show differential expression in a single figure.
- Coding a polished, labeled plot in R or Python takes time and iteration.
- Fold-change and significance thresholds make the important hits obvious to any reader.
- Researchers need clean, presentable figures for papers, posters, and talks quickly.
- Regenerating from a description is faster than re-running a script every time labels or cutoffs change.
How to make a volcano plot
Describe your comparison (for example treated vs control), say which axes and units you want — log2 fold change against −log10 p-value or adjusted p-value — and set your thresholds, such as |log2FC| > 1 and padj < 0.05. Note which genes or proteins to label and how to color up- and down-regulated points. Generate the figure, then check the thresholds, colors, and labels, and refine until it matches your data.
Parts of a volcano plot
- X-axis — log2 fold change (effect size and direction).
- Y-axis — −log10 p-value or adjusted p-value (significance).
- Threshold lines — dashed cutoffs for fold change and significance.
- Up-regulated points — significant features with positive fold change.
- Down-regulated points — significant features with negative fold change.
- Gene labels — names of the top or selected features of interest.
Volcano Plot Generator FAQ
What is a volcano plot?
A volcano plot is a scatter plot used to show differential expression. It plots log2 fold change on the x-axis against statistical significance (−log10 p-value) on the y-axis, so the most significant, largest-change features appear toward the top left and top right.
What data do I need for a volcano plot?
For each feature (gene, protein, or metabolite) you need a fold change (usually log2) and a p-value or adjusted p-value. Describe these along with your thresholds, and you can name specific features you want labeled.
Can I label specific genes?
Yes. Name the genes or proteins you want annotated in your description and the generator highlights and labels them; you can also ask it to label the top N most significant features.
Can I use adjusted p-values?
Yes. You can specify adjusted p-values (for example FDR or padj) for the y-axis and set the significance cutoff, such as padj < 0.05, in your prompt.
Is it suitable for RNA-seq and proteomics?
Yes. The same volcano plot format works for RNA-seq, proteomics, single-cell marker comparisons, metabolomics, and other differential-abundance analyses — just describe the data and thresholds.
Can I export an editable figure?
SciDraw AI can export to vector formats so you can adjust labels, colors, and thresholds afterwards. Always check the figure against your underlying data before submitting.
Explore More Tools
Need other research figures?
Generate forest plots, survival curves, pathway diagrams, and other research figures with SciDraw AI.


