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()