GPTomics/bio-data-visualization-volcano-customization

Version Compatibility

Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+, numpy 1.26+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Volcano Plot Customization

"Create a volcano plot" → Plot log2 fold change vs -log10 p-value from differential expression results, highlighting significant genes.

• R: EnhancedVolcano::EnhancedVolcano(), ggplot2 with manual thresholds
• Python: matplotlib.scatter() with color-coded significance categories

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

Goal: Add non-overlapping gene name labels to a volcano plot for the top significant genes or genes of interest.

Approach: Filter for top genes by p-value, use ggrepel to place text labels with automatic repulsion to avoid overlaps, and optionally highlight specific genes of interest with larger points.

# 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