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.
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