Volcano Plot
Chart overview
A volcano plot is the standard visualization in transcriptomics and proteomics for displaying the results of differential expression analysis.
Key points
- The x-axis shows log2 fold-change between conditions, and the y-axis shows -log10 adjusted p-value.
- Points in the upper-left and upper-right quadrants represent significantly downregulated and upregulated features respectively.
- The shape resembles a volcano, with the most statistically significant and biologically meaningful changes erupting from the base.
Example Visualization
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Generate publication-ready volcano plots with AI in seconds. No coding required – just describe your data and let AI do the work.
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"Create a volcano plot from my RNA-seq differential expression results. Plot log2 fold-change on the x-axis and -log10 adjusted p-value on the y-axis. Color upregulated genes (log2FC > 1, padj < 0.05) red, downregulated genes (log2FC < -1, padj < 0.05) blue, and non-significant genes grey. Add dashed threshold lines at log2FC = ±1 and padj = 0.05. Label the top 10 most significant genes by name. Format for Nature publication at 300 DPI."
How to create this chart in 30 seconds
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AI Generation
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Python Code Example
Console Output
Figure saved: plotivy-volcano-plot.png
Common Use Cases
- 1RNA-seq differential expression analysis (DESeq2, edgeR output visualization)
- 2Proteomics: visualizing differentially abundant proteins from mass spectrometry
- 3GWAS: displaying significant SNP associations across the genome
- 4Drug screening: identifying active compounds from high-throughput assays
Pro Tips
Use adjusted p-values (FDR/BH correction) not raw p-values on the y-axis
Cap the y-axis maximum to prevent extreme outliers from distorting the scale
Label only the most significant or biologically relevant genes to avoid clutter
Use colorblind-safe colors (e.g., orange/blue instead of red/green)
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.