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

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

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 =  pch  

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   title   
    theme_bw 
    themelegend.position  

edgeR MA Plot

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

Volcano Plot

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   color   
    geom_hlineyintercept  log10 linetype   color   
    labsx   y   title   
    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    
    geom_text_repeldata  top_genes aeslabel  gene
                    size   max.overlaps   
    geom_vlinexintercept    linetype   
    geom_hlineyintercept  log10 linetype   
    labsx   y   
    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

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' 
    ylabpaste0 percentVar  
    ggtitle 
    theme_bw 
    themelegend.position  

edgeR PCA (via limma)

library(limma)
log_cpm <- cpm(y, log = TRUE)
plotMDS(log_cpm, col = as.numeric(group), pch = 16)
legend('topright', legend = levels(group), col = 1:nlevels(group), pch = 16)

Heatmaps

Top DE Genes Heatmap

library(pheatmap)

# Get top significant genes
sig_genes <- rownames(subset(res, padj < 0.01))

# Get normalized counts
vsd <- vst(dds, blind = FALSE)
mat <- assay(vsd)[sig_genes, ]

# Scale by row (z-score)
mat_scaled <- t(scale(t(mat)))

# Create annotation
annotation_col <- data.frame(
    condition = colData(dds)$condition,
    row.names = colnames(mat)
)

pheatmap(mat_scaled,
         annotation_col = annotation_col,
         show_rownames = FALSE,
         clustering_distance_rows = 'correlation'
         clustering_distance_cols  
         color  colorRampPalette  
         main  

Sample Distance Heatmap

vsd <- vst(dds, blind = FALSE)

# Calculate sample distances
sampleDists <- dist(t(assay(vsd)))
sampleDistMatrix <- as.matrix(sampleDists)

# Annotation
annotation <- data.frame(
    condition = colData(dds)$condition,
    row.names = colnames(dds)
)

pheatmap(sampleDistMatrix,
         annotation_col = annotation,
         annotation_row = annotation,
         clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists,
         color = colorRampPalette(c('white', 'steelblue'))(100),
         main = 

Gene Expression Heatmap

# Select genes of interest
genes_of_interest <- c('gene1', 'gene2', 'gene3', 'gene4', 'gene5')
mat <- assay(vsd)[genes_of_interest, ]

pheatmap(mat,
         scale = 'row',
         annotation_col = annotation_col,
         show_rownames = TRUE,
         cluster_cols = TRUE,
         cluster_rows = TRUE,
         main = 'Genes of Interest')

Dispersion Plot

DESeq2

plotDispEsts(dds, main = 'Dispersion Estimates')

edgeR

plotBCV(y, main = 'Biological Coefficient of Variation')

Counts Plot for Individual Genes

DESeq2

# Plot counts for a specific gene
plotCounts(dds, gene = 'GENE_NAME', intgroup = 'condition')

# With ggplot2
d <- plotCounts(dds, gene = 'GENE_NAME', intgroup = 'condition', returnData = TRUE)
ggplot(d, aes(x = condition, y = count, color = condition)) +
    geom_point(position = position_jitter(width = 0.1), size = 3) +
    scale_y_log10() +
    ggtitle('GENE_NAME Expression') +
    theme_bw()

edgeR

# Get CPM for a gene
gene_idx <- which(rownames(y) == 'GENE_NAME')
cpm_gene <- cpm(y)[gene_idx, ]

# Plot
df <- data.frame(cpm = cpm_gene, group = group)
ggplot(df, aes(x = group, y = cpm, color = group)) +
    geom_point(position = position_jitter(width = 0.1), size = 3) +
    scale_y_log10() +
    labs(y = 'CPM', title = 'GENE_NAME Expression') +
    theme_bw()

P-value Histogram

# Check p-value distribution (should be uniform under null with peak near 0)
res_df <- as.data.frame(res)
ggplot(res_df, aes(x = pvalue)) +
    geom_histogram(bins = 50, fill = 'steelblue', color = 'white') +
    labs(x = 'P-value', y = 'Frequency', title = 'P-value Distribution') +
    theme_bw()

Saving Plots

# Save as PDF (vector)
pdf('volcano_plot.pdf', width = 8, height = 6)
# ... plot code ...
dev.off()

# Save as PNG (raster)
png('volcano_plot.png', width = 800, height = 600, res = 150)
# ... plot code ...
dev.off()

# Using ggsave for ggplot objects
p <- ggplot(...) + ...
ggsave('plot.pdf', p, width = 8, height = 6)
ggsave('plot.png', p, width = 8, height = 6, dpi = 300)

Color Palettes

# For heatmaps
library(RColorBrewer)

# Diverging (for expression: blue-white-red)
colorRampPalette(rev(brewer.pal(n = 7, name = 'RdBu')))(100)

# Sequential (for distances)
colorRampPalette(brewer.pal(n = 9, name = 'Blues'))(100)

# For categorical groups
brewer.pal(n = 8, name = 'Set1')

Quick Reference: Common Plots

Plot	Purpose	Function
MA plot	LFC vs mean expression	plotMA(), plotMD()
Volcano	LFC vs significance	ggplot2, EnhancedVolcano
PCA	Sample clustering	plotPCA(), plotMDS()
Heatmap	Gene patterns	pheatmap()
Dispersion	Model fit	plotDispEsts(), plotBCV()
Counts	Individual genes	plotCounts()

Related Skills

• deseq2-basics - Generate DESeq2 results for visualization
• edger-basics - Generate edgeR results for visualization
• de-results - Filter genes before visualization
• data-visualization/specialized-omics-plots - Custom ggplot2 volcano/MA/PCA functions
• data-visualization/heatmaps-clustering - Advanced heatmap customization