How to Make a Volcano Plot for Scientific Papers
A complete guide to creating publication-quality volcano plots. From differential expression data to journal-ready export.
What is a scientific volcano plot?
A volcano plot is a scatter plot used in transcriptomics and proteomics to visualize differential expression results. It plots log2 fold change on the x-axis and statistical significance (-log10 p-value) on the y-axis. Each point represents a gene or protein. Points in the top-left and top-right are both statistically significant and biologically meaningful.
Key requirements:
- • X-axis: log2 fold change (log2FC)
- • Y-axis: -log10(p-value) or -log10(adjusted p-value)
- • Threshold lines: |log2FC| and p-value cutoffs
- • Color coding: upregulated, downregulated, not significant
- • Top gene labels (10–20 most significant)
- • Journal width (single: 84–90 mm, double: 170–183 mm)
- • 300 DPI minimum for raster export
Step-by-Step Guide
- Compute fold changes and p-values
Run differential expression analysis (DESeq2, edgeR, limma) to get log2FC and p-values for all genes/proteins.
- Set thresholds
Choose fold change and p-value thresholds. Common: |log2FC| > 1 and p < 0.05. Use adjusted p-values for multiple testing.
- Plot data points
X-axis: log2FC. Y-axis: -log10(p-value). Each point is a gene or protein. Use semi-transparent markers for dense data.
- Add threshold lines
Draw vertical lines at log2FC = ±1 (or chosen threshold). Draw horizontal line at -log10(p) = 1.3 (p=0.05).
- Color by significance
Upregulated (red): log2FC > 1 and p < 0.05. Downregulated (blue): log2FC < -1 and p < 0.05. Not significant (gray): all others.
- Export for publication
Set width to journal column or double column. Export at 300 DPI. Ensure labels are readable. Add a legend explaining colors and thresholds.
Interpreting a Volcano Plot
- ✓Top-right — Significantly upregulated (high log2FC, low p-value)
- ✓Top-left — Significantly downregulated (negative log2FC, low p-value)
- ✓Bottom — Not statistically significant (high p-value regardless of fold change)
- ✓Center — Not biologically meaningful (small fold change regardless of p-value)
Frequently Asked Questions
How do you make a volcano plot for a scientific paper?
To make a volcano plot: (1) compute fold changes (log2FC) and p-values for all genes/proteins, (2) plot log2FC on the x-axis and -log10(p-value) on the y-axis, (3) add horizontal and vertical threshold lines (e.g., |log2FC| > 1 and p < 0.05), (4) color points by significance (upregulated, downregulated, not significant), (5) label top genes, (6) add axis labels, (7) export at 300 DPI at journal width.
What is a volcano plot used for?
A volcano plot is used in transcriptomics and proteomics to visualize the results of differential expression analysis. It shows both the magnitude of change (fold change) and statistical significance (p-value) for thousands of genes or proteins simultaneously. Volcano plots are standard for RNA-seq, microarray, and proteomics papers.
What do the axes represent in a volcano plot?
The x-axis represents log2 fold change (log2FC): positive values mean upregulation, negative values mean downregulation. The y-axis represents -log10(p-value): higher values mean more significant. Points in the top-left and top-right corners are the most statistically significant and biologically meaningful changes.
What is the best tool for making volcano plots?
FigureGuild is ideal for publication-ready volcano plots. It auto-computes thresholds, colors points by significance, and applies journal formatting. R (ggplot2) and Python (matplotlib) are also used but require coding. Most RNA-seq pipelines (DESeq2, edgeR) generate volcano plots but with basic formatting.
How do you set thresholds in a volcano plot?
Common thresholds are |log2FC| > 1 (2-fold change) and p < 0.05. Adjust based on your field: some studies use |log2FC| > 0.5 and p < 0.01. Always state your thresholds in the figure legend. Use adjusted p-values (FDR/Benjamini-Hochberg) for multiple testing correction.
Should volcano plots label individual genes?
Yes, label the top 10–20 most significant genes or genes of biological interest. Too many labels make the plot unreadable. Use clear, non-overlapping labels. Consider providing a separate table with all significant genes as supplementary data.
Related Pages
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