Data & ReproScientific VisualizationFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-data-visualization-volcano-customization

Maintainer FreedomIntelligence · Last updated April 1, 2026

Customized volcano plots with ggplot2 or matplotlib for DE results.

OpenClawNanoClawAnalysisWritingbio-data-visualization-volcano-customization🧠 bioos extended suitebioos extended bioinformatics suitecustomized

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-data-visualization-volcano-customization

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

df$significance <- case_when( df$padj < 0.05 & df$log2FoldChange > 1 ~ 'Up', df$padj < 0.05 & df$log2FoldChange < -1 ~ 'Down', TRUE ~ 'NS' ).
ggplot(df, aes(x = log2FoldChange, y = -log10(pvalue))) + geom_point(aes(color = significance), alpha = 0.6, size = 1.5) + scale_color_manual(values = c(Up = '#E64B35', Down = '#4DBBD5', NS = 'gray70')) + geom_hline(yintercept = -log10(0.05), linetype = 'dashed', color = 'gray40') + geom_vline(xintercept = c(-1, 1), linetype = 'dashed', color = 'gray40') + theme_classic() + labs(x = 'log2 Fold Change', y = '-log10(p-value)', color = 'Regulation').
top_genes <- df %>% filter(padj < 0.05, abs(log2FoldChange) > 1) %>% arrange(pvalue) %>% head(20).
ggplot(df, aes(x = log2FoldChange, y = -log10(pvalue))) + geom_point(aes(color = significance), alpha = 0.6, size = 1.5) + scale_color_manual(values = c(Up = '#E64B35', Down = '#4DBBD5', NS = 'gray70')) + geom_text_repel( data = top_genes, aes(label = gene), size = 3, max.overlaps = 20, box.padding = 0.5, segment.color = 'gray50' ) + theme_classic().

Source Doc

Excerpt From SKILL.md

ggplot2 Basic Volcano

library(ggplot2)
library(ggrepel)

## Label specific genes of interest

genes_of_interest <- c('TP53', 'BRCA1', 'MYC', 'EGFR')
highlight_df <- df %>% filter(gene %in% genes_of_interest)

ggplot(df, aes(x = log2FoldChange, y = -log10(pvalue))) +
    geom_point(aes(color = significance), alpha = 0.4, size = 1.5) +
    geom_point(data = highlight_df, color = 'black', size = 3) +
    geom_text_repel(data = highlight_df, aes(label = gene), fontface = 'bold') +
    theme_classic()

EnhancedVolcano (R)

library(EnhancedVolcano)

Use cases

Use bio-data-visualization-volcano-customization to prepare publication-quality figures.
Apply bio-data-visualization-volcano-customization when results need clear visual communication.