What is a volcano plot?

A volcano plot is a scatter plot that shows statistical significance (−log10 p-value) on the Y-axis and biological effect size (log2 fold change) on the X-axis. It's the standard way to visualize differential expression results from RNA-seq, proteomics, or other omics experiments. The name comes from the shape — significant genes with large fold changes form two "eruptions" on either side.

Can I make a volcano plot without R or Python?

Yes. FigCanvas lets you upload a CSV and generate a volcano plot from a text description — no coding required. You can also download the Python code behind the figure afterward if you need it for reproducibility.

How do I make a volcano plot from DESeq2 output?

Export your DESeq2 results table as a CSV (it should have columns like `log2FoldChange` and `padj`). Upload that file to FigCanvas, select the volcano plot option, and hit Generate. FigCanvas auto-detects the relevant columns.

What file formats does FigCanvas accept for volcano plots?

CSV, TSV, TXT, and Excel (.xlsx) files. As long as your file has columns for gene ID, fold change, and p-value, it will work.

Can I customize the colors and labels on my volcano plot?

Yes. After generating the initial plot, use the Prompt box to make changes — for example, "label only the top 10 genes" or "use blue and red instead of the default colors." You can also switch to colorblind-safe palettes like Okabe-Ito.

What export formats are available?

You can export as PNG, SVG, or PDF. You can also download the Python code that generated the plot. --- {/* ============================================ IMAGE CHECKLIST — Do not publish this section ============================================ */} {/* IMAGE 1 — Hero File: /blog/how-to-make-a-volcano-plot/hero.webp Source: User-provided dataPlot-export-1776213307450.png (T cell volcano) Dimensions: 1200×630 (pad square original to OG size with white background) Notes: Use as-is. Keep title/labels readable at 1200px. IMAGE 2 — Threshold variant File: /blog/how-to-make-a-volcano-plot/variant-threshold.webp Source: User-provided dataPlot-export-1776213331745.png Dimensions: 900×900 square, preserve aspect Notes: Use as-is. IMAGE 3 — Editor overview (Step 1) File: /blog/how-to-make-a-volcano-plot/step1-editor-overview.webp Source: User-provided 截屏2026-04-14_下午8_33_56.png Dimensions: 1400×800 native Notes: Optional red annotation arrow on "Data Plot" button. IMAGE 4 — Iterate panel + export (Step 3) File: /blog/how-to-make-a-volcano-plot/step3-iterate-panel.webp Source: User-provided 截屏2026-04-14_下午8_34_25.png Dimensions: 1400×800 native Notes: STRONGLY recommend two annotation boxes — one on Prompt textbox ("Instant Iterations"), one on context menu ("Export"). This image does double duty. =============================================== SHOULD-CREATE (improves clarity, low effort) =============================================== IMAGE 5 — CSV format example (optional, insert at top of Step 1) File: /blog/how-to-make-a-volcano-plot/csv-format.webp Status: NEEDS CREATION What to capture: 5-row preview of DESeq2 CSV showing columns gene_symbol, log2FoldChange, pvalue, padj. Use real gene names (CTLA4, TCF7, IL7R) matching the hero. Highlight the 3 required columns. Dimensions: 1000×400 Notes: Makes Step 1 concrete. Can be a styled HTML table screenshot. Skip if tight on time — text already covers it. */}

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April 14, 2026· 8 min read

How to Make a Volcano Plot in 3 Minutes (No R, No Python)

Make a publication-ready volcano plot from a CSV in 3 minutes — no R, no ggplot2, no Python. Step-by-step tutorial with AI prompts and 300 DPI export.

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