name: bio-data-visualization-volcano-customization
description: Create publication-ready volcano plots with custom thresholds, gene labels, and highlighting using ggplot2, EnhancedVolcano, or matplotlib. Use when visualizing differential expression or association results with gene annotations.
tool_type: mixed
primary_tool: ggplot2
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
Volcano Plot Customization
ggplot2 Basic Volcano
library(ggplot2)
library(ggrepel)
# Add significance category column
df$significance <- case_when(
df$padj < 0.05 & df$log2FoldChange > 1 ~ 'Up',
df$padj < 0.05 & df$log2FoldChange < -1 ~ 'Down',
TRUE ~ 'NS'
)
ggplot(df, aes(x = log2FoldChange, y = -log10(pvalue))) +
geom_point(aes(color = significance), alpha = 0.6, size = 1.5) +
scale_color_manual(values = c(Up = '#E64B35', Down = '#4DBBD5', NS = 'gray70')) +
geom_hline(yintercept = -log10(0.05), linetype = 'dashed', color = 'gray40') +
geom_vline(xintercept = c(-1, 1), linetype = 'dashed', color = 'gray40') +
theme_classic() +
labs(x = 'log2 Fold Change', y = '-log10(p-value)', color = 'Regulation')
ggplot2 with Gene Labels
# Label top significant genes
top_genes <- df %>%
filter(padj < 0.05, abs(log2FoldChange) > 1) %>%
arrange(pvalue) %>%
head(20)
ggplot(df, aes(x = log2FoldChange, y = -log10(pvalue))) +
geom_point(aes(color = significance), alpha = 0.6, size = 1.5) +
scale_color_manual(values = c(Up = '#E64B35', Down = '#4DBBD5', NS = 'gray70')) +
geom_text_repel(
data = top_genes,
aes(label = gene),
size = 3,
max.overlaps = 20,
box.padding = 0.5,
segment.color = 'gray50'
) +
theme_classic()
# Label specific genes of interest
genes_of_interest <- c('TP53', 'BRCA1', 'MYC', 'EGFR')
highlight_df <- df %>% filter(gene %in% genes_of_interest)
ggplot(df, aes(x = log2FoldChange, y = -log10(pvalue))) +
geom_point(aes(color = significance), alpha = 0.4, size = 1.5) +
geom_point(data = highlight_df, color = 'black', size = 3) +
geom_text_repel(data = highlight_df, aes(label = gene), fontface = 'bold') +
theme_classic()
EnhancedVolcano (R)
library(EnhancedVolcano)
# Basic EnhancedVolcano
EnhancedVolcano(df,
lab = df$gene,
x = 'log2FoldChange',
y = 'pvalue',
pCutoff = 0.05,
FCcutoff = 1,
title = 'Treatment vs Control',
subtitle = 'DE genes highlighted')
# Customized EnhancedVolcano
EnhancedVolcano(df,
lab = df$gene,
x = 'log2FoldChange',
y = 'pvalue',
pCutoff = 0.05,
FCcutoff = 1,
xlim = c(-5, 5),
ylim = c(0, 50),
pointSize = 2,
labSize = 3,
colAlpha = 0.6,
col = c('gray70', '#4DBBD5', '#00A087', '#E64B35'),
legendLabels = c('NS', 'Log2FC', 'p-value', 'p-value and Log2FC'),
legendPosition = 'right',
drawConnectors = TRUE,
widthConnectors = 0.5,
maxoverlapsConnectors = 20,
selectLab = genes_of_interest, # Only label specific genes
boxedLabels = TRUE)
EnhancedVolcano with Custom Keyvals
# Custom point colors by category
keyvals <- ifelse(df$log2FoldChange > 2 & df$padj < 0.01, '#E64B35',
ifelse(df$log2FoldChange < -2 & df$padj < 0.01, '#4DBBD5',
ifelse(df$padj < 0.05, '#00A087', 'gray70')))
names(keyvals)[keyvals == '#E64B35'] <- 'Highly Up'
names(keyvals)[keyvals == '#4DBBD5'] <- 'Highly Down'
names(keyvals)[keyvals == '#00A087'] <- 'Moderate'
names(keyvals)[keyvals == 'gray70'] <- 'NS'
EnhancedVolcano(df,
lab = df$gene,
x = 'log2FoldChange',
y = 'pvalue',
colCustom = keyvals,
legendPosition = 'right')
matplotlib Volcano (Python)
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(8, 6))
# Color by significance
colors = np.where((df['padj'] < 0.05) & (df['log2FoldChange'] > 1), '#E64B35',
np.where((df['padj'] < 0.05) & (df['log2FoldChange'] < -1), '#4DBBD5', 'gray'))
ax.scatter(df['log2FoldChange'], -np.log10(df['pvalue']),
c=colors, alpha=0.6, s=20, edgecolors='none')
# Threshold lines
ax.axhline(-np.log10(0.05), color='gray', linestyle='--', linewidth=1)
ax.axvline(-1, color='gray', linestyle='--', linewidth=1)
ax.axvline(1, color='gray', linestyle='--', linewidth=1)
ax.set_xlabel('log2 Fold Change')
ax.set_ylabel('-log10(p-value)')
plt.tight_layout()
matplotlib with Labels
from adjustText import adjust_text
# Get top genes to label
top_idx = df.nsmallest(15, 'pvalue').index
fig, ax = plt.subplots(figsize=(10, 8))
ax.scatter(df['log2FoldChange'], -np.log10(df['pvalue']), c=colors, alpha=0.5, s=15)
# Add labels with adjust_text to avoid overlaps
texts = []
for idx in top_idx:
texts.append(ax.text(df.loc[idx, 'log2FoldChange'],
-np.log10(df.loc[idx, 'pvalue']),
df.loc[idx, 'gene'],
fontsize=8))
adjust_text(texts, arrowprops=dict(arrowstyle='-', color='gray', lw=0.5))
plt.tight_layout()
Threshold Customization
# Standard thresholds
# FC > 1 (2-fold change): Common for RNA-seq, may miss subtle changes
# FC > 0.58 (~1.5-fold): More sensitive, use for subtle effects
# padj < 0.05: Standard FDR threshold
# padj < 0.01: Stringent, fewer false positives
# padj < 0.1: Relaxed, use for exploratory analysis
# Adjust thresholds based on your data
pval_threshold <- 0.05
fc_threshold <- 1 # log2 scale
df$significance <- case_when(
df$padj < pval_threshold & df$log2FoldChange > fc_threshold ~ 'Up',
df$padj < pval_threshold & df$log2FoldChange < -fc_threshold ~ 'Down',
TRUE ~ 'NS'
)
Save Publication-Ready Volcano
# R - high resolution
ggsave('volcano.pdf', width = 8, height = 6)
ggsave('volcano.png', width = 8, height = 6, dpi = 300)
# EnhancedVolcano returns ggplot object
p <- EnhancedVolcano(df, lab = df$gene, x = 'log2FoldChange', y = 'pvalue')
ggsave('volcano.pdf', p, width = 10, height = 8)
# Python
plt.savefig('volcano.pdf', bbox_inches='tight')
plt.savefig('volcano.png', dpi=300, bbox_inches='tight')
Related Skills
- differential-expression/de-visualization - DE-specific plots
- data-visualization/ggplot2-fundamentals - General ggplot2
- data-visualization/color-palettes - Color selection
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