Visualize differences in gene expression using DESeq2 and edgeR results. Create plots of expression changes, sample similarities, individual genes, and statistical distributions to inspect analysis results and export figures as PDF or PNG.
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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.
安装前可以查看交给 AI 代理的指令内容。
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:
packageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Create visualizations for differential expression analysis using DESeq2 and edgeR built-in plotting functions.
This skill covers DE-specific built-in functions:
plotMA(), plotPCA(), plotDispEsts(), plotCounts()plotMD(), plotBCV(), plotMDS()For custom ggplot2/matplotlib implementations of volcano, MA, and PCA plots, see data-visualization/specialized-omics-plots.
library(DESeq2)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(ggrepel) # For labeled points
install.packages(c('ggplot2', 'pheatmap', 'RColorBrewer', 'ggrepel'))
# Optional: Enhanced volcano plots
BiocManager::install('EnhancedVolcano')
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.
# 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))
res_df <- as.data.frame(res)
res_df$significant <- res_df$padj < 0.05 & !is.na(res_df$padj)
ggplot(res_df, aes(x = log10(baseMean), y = log2FoldChange, color = significant)) +
geom_point(alpha = 0.5, size = 1) +
scale_color_manual(values = c('grey60', 'red')) +
geom_hline(yintercept = 0, linetype = 'dashed') +
labs(x = 'log10(Mean Expression)', y = 'log2 Fold Change', title = 'MA Plot') +
theme_bw() +
theme(legend.position = 'bottom')
# Using plotMD (mean-difference plot)
plotMD(qlf, main = 'MD Plot')
abline(h = c(-1, 1), col = 'blue', lty = 2)
Goal: Display statistical significance against fold change magnitude to identify the most important DE genes.
Approach: Plot -log10(p-value) vs. log2 fold change with threshold lines and optional gene labels.
"Create a volcano plot of differentially expressed genes" → Scatter plot of fold change vs. significance with colored significance regions and labeled top hits.
res_df <- as.data.frame(res)
res_df$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()
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()
library(EnhancedVolcano)
EnhancedVolcano(res,
lab = rownames(res),
x = 'log2FoldChange',
y = 'pvalue',
pCutoff = 0.05,
FCcutoff = 1,
title = 'Differential Expression',
subtitle = 'Treatment vs Control')
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.
# 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)
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 = 'right')
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)
Goal: Visualize expression patterns of significant genes across samples to reveal clusters and condition effects.
Approach: Z-score normalize VST-transformed counts for significant genes and cluster with pheatmap, annotating by condition.
"Make a heatmap of the top differentially expressed genes" → Extract significant genes, z-score normalize, and create a clustered heatmap with sample annotations.
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 = 'correlation',
color = colorRampPalette(c('blue', 'white', 'red'))(100),
main = 'Top DE Genes')
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 = 'Sample Distance Matrix')
# 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')
Goal: Assess the fit of the dispersion model to verify DE analysis assumptions.
Approach: Plot gene-wise, fitted, and final dispersion estimates against mean expression.
plotDispEsts(dds, main = 'Dispersion Estimates')
plotBCV(y, main = 'Biological Coefficient of Variation')
Goal: Visualize expression of a specific gene across samples and conditions.
Approach: Extract per-sample counts for a gene and plot by condition using plotCounts or ggplot2.
# 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()
# 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()
Goal: Diagnose the quality of the DE analysis by examining the raw p-value distribution.
Approach: Histogram of raw p-values; a uniform distribution with a peak near zero indicates a well-calibrated test.
# 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()
Goal: Export publication-quality plots in vector or raster formats.
Approach: Use pdf/png devices or ggsave with appropriate resolution and dimensions.
# 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)
Goal: Select appropriate color schemes for heatmaps and categorical data.
Approach: Use RColorBrewer palettes -- diverging for expression, sequential for distances, qualitative for groups.
# 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')
| 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() |
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