/bio-de-visualization

name: bio-de-visualization
description: Visualize differential expression results using DESeq2/edgeR built-in functions. Covers plotMA, plotDispEsts, plotCounts, plotBCV, sample distance heatmaps, and p-value histograms. Use when visualizing differential expression results.
tool_type: r
primary_tool: DESeq2

Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, ggplot2 3.5+, limma 3.58+, matplotlib 3.8+

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

• 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.

DE Visualization

Create visualizations for differential expression analysis using DESeq2 and edgeR built-in plotting functions.

Scope

This skill covers **DE-specific built-in functions**:

• DESeq2: `plotMA()`, `plotPCA()`, `plotDispEsts()`, `plotCounts()`
• edgeR: `plotMD()`, `plotBCV()`, `plotMDS()`
• Sample distance heatmaps and p-value distributions

**For custom ggplot2/matplotlib implementations** of volcano, MA, and PCA plots, see `data-visualization/specialized-omics-plots`.

Required Libraries

library(DESeq2)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(ggrepel)  # For labeled points

Installation

install.packages(c('ggplot2', 'pheatmap', 'RColorBrewer', 'ggrepel'))
# Optional: Enhanced volcano plots
BiocManager::install('EnhancedVolcano')

MA Plot

**Goal:** Visualize the relationship between mean expression and log fold change to assess DE results.

**Approach:** Plot log fold change against mean normalized counts, highlighting significant genes.

**"Make an MA plot of my DE results"** → Plot mean expression vs. fold change with significant genes colored, using plotMA or ggplot2.

DESeq2 MA Plot

# Built-in MA plot
plotMA(res, ylim = c(-5, 5), main = 'MA Plot')

# With custom alpha
plotMA(res, alpha = 0.05, ylim = c(-5, 5))

# Highlight specific genes
plotMA(res, ylim = c(-5, 5))
with(subset(res, padj < 0.01 & abs(log2FoldChange) > 2),
     points(baseMean, log2FoldChange, col = 'red', pch = 20))

Custom ggplot2 MA Plot

res_df <- as.data.frame(res)
res_df$significant <- res_df$padj < 0.05 & !is.na(res_df$padj)

ggplot(res_df, aes(x = log10(baseMean), y = log2FoldChange, color = significant)) +
    geom_point(alpha = 0.5, size = 1) +
    scale_color_manual(values = c('grey60', 'red')) +
    geom_hline(yintercept = 0, linetype = 'dashed') +
    labs(x = 'log10(Mean Expression)', y = 'log2 Fold Change', title = 'MA Plot') +
    theme_bw() +
    theme(legend.position = 'bottom')

edgeR MA Plot

# Using plotMD (mean-difference plot)
plotMD(qlf, main = 'MD Plot')
abline(h = c(-1, 1), col = 'blue', lty = 2)

Volcano Plot

**Goal:** Display statistical significance against fold change magnitude to identify the most important DE genes.

**Approach:** Plot -log10(p-value) vs. log2 fold change with threshold lines and optional gene labels.

**"Create a volcano plot of differentially expressed genes"** → Scatter plot of fold change vs. significance with colored significance regions and labeled top hits.

Basic Volcano Plot

res_df <- as.data.frame(res)
res_df$significant <- res_df$padj < 0.05 & abs(res_df$log2FoldChange) > 1

ggplot(res_df, aes(x = log2FoldChange, y = -log10(pvalue), color = significant)) +
    geom_point(alpha = 0.5, size = 1) +
    scale_color_manual(values = c('grey60', 'red')) +
    geom_vline(xintercept = c(-1, 1), linetype = 'dashed', color = 'blue') +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed', color = 'blue') +
    labs(x = 'log2 Fold Change', y = '-log10(p-value)', title = 'Volcano Plot') +
    theme_bw()

Volcano with Gene Labels

res_df <- as.data.frame(res)
res_df$gene <- rownames(res_df)
res_df$significant <- res_df$padj < 0.05 & abs(res_df$log2FoldChange) > 1

# Label top genes
top_genes <- head(res_df[order(res_df$padj), ], 10)

ggplot(res_df, aes(x = log2FoldChange, y = -log10(pvalue))) +
    geom_point(aes(color = significant), alpha = 0.5, size = 1) +
    scale_color_manual(values = c('grey60', 'red')) +
    geom_text_repel(data = top_genes, aes(label = gene),
                    size = 3, max.overlaps = 20) +
    geom_vline(xintercept = c(-1, 1), linetype = 'dashed') +
    geom_hline(yintercept = -log10(0.05), linetype = 'dashed') +
    labs(x = 'log2 Fold Change', y = '-log10(p-value)') +
    theme_bw()

EnhancedVolcano

library(EnhancedVolcano)

EnhancedVolcano(res,
    lab = rownames(res),
    x = 'log2FoldChange',
    y = 'pvalue',
    pCutoff = 0.05,
    FCcutoff = 1,
    title = 'Differential Expression',
    subtitle = 'Treatment vs Control')

PCA Plot

**Goal:** Assess sample clustering and identify batch effects or outliers via dimensionality reduction.

**Approach:** Apply variance-stabilizing transformation then project samples onto principal components, coloring by experimental variables.

**"Show me a PCA plot of my samples"** → Perform PCA on transformed expression data and visualize sample separation by condition and batch.

DESeq2 PCA

# Variance stabilizing transformation first
vsd <- vst(dds, blind = FALSE)

# Basic PCA
plotPCA(vsd, intgroup = 'condition')

# With more options
plotPCA(vsd, intgroup = c('condition', 'batch'), ntop = 500)

Custom PCA with ggplot2

vsd <- vst(dds, blind = FALSE)
pca_data <- plotPCA(vsd, intgroup = c('condition', 'batch'), returnData = TRUE)
percentVar <- round(100 * attr(pca_data, 'percentVar'))

ggplot(pca_data, aes(x = PC1, y = PC2, color = condition, shape = batch)) +
    geom_point(size = 4) +
    xlab(paste0('PC1: ', percentVar[1], '% variance')) +
    ylab(paste0('PC2: ', percentVar[2], '% variance')) +
    ggtitle('PCA Plot') +
    theme_bw() +
    theme(legend.position =