Volcano Plot for Statistical Significance in Python, R, Julia and JavaScript

Source code in 15 libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts.

A volcano plot displays statistical significance (-log10 p-value) on the y-axis versus effect size (log2 fold change) on the x-axis. Points are typically color-coded to highlight features that are both statistically significant and have large effect sizes. This visualization is essential for quickly identifying the most important changes in differential expression, proteomics, and genomics studies.

Renders

• Full-resolution render, light theme
• Full-resolution render, dark theme
• Interactive version, light theme
• Interactive version, dark theme

Preview render from the Plotly implementation.

Implementations

• Volcano Plot for Statistical Significance in Altair (Python)
• Volcano Plot for Statistical Significance in Bokeh (Python)
• Volcano Plot for Statistical Significance in lets-plot (Python)
• Volcano Plot for Statistical Significance in Matplotlib (Python)
• Volcano Plot for Statistical Significance in Plotly (Python)
• Volcano Plot for Statistical Significance in plotnine (Python)
• Volcano Plot for Statistical Significance in Pygal (Python)
• Volcano Plot for Statistical Significance in Seaborn (Python)
• Volcano Plot for Statistical Significance in ggplot2 (R)
• Volcano Plot for Statistical Significance in Makie.jl (Julia)
• Volcano Plot for Statistical Significance in Chart.js (JavaScript)
• Volcano Plot for Statistical Significance in D3.js (JavaScript)
• Volcano Plot for Statistical Significance in Apache ECharts (JavaScript)
• Volcano Plot for Statistical Significance in Highcharts (JavaScript)
• Volcano Plot for Statistical Significance in MUI X Charts (JavaScript)