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Creating a Volcano Plot from DESeq2 Analysis
January 2, 2025 Off By adminTable of Contents
ToggleStep-by-Step Instructions
1. Install Required Packages Ensure the following R packages are installed:
2. Perform Differential Expression Analysis Run DESeq2 for your dataset:
library(DESeq2)# Load your count matrix and metadata
counts <- read.csv("counts.csv", row.names = 1)
metadata <- read.csv("metadata.csv")
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = metadata,
design = ~ condition)
dds <- DESeq(dds)
res <- results(dds)
3. Prepare Data for Plotting Extract necessary values (log2 fold change and adjusted p-values):
res_df <- as.data.frame(res)
res_df$log2FoldChange[is.na(res_df$log2FoldChange)] <- 0
res_df$padj[is.na(res_df$padj)] <- 1 # Assign non-significant values# Thresholds for up/down-regulated genes
log2FC_threshold <- 1.5
pval_threshold <- 0.05
res_df$significance <- ifelse(
res_df$padj < pval_threshold & res_df$log2FoldChange > log2FC_threshold, "Upregulated",
ifelse(res_df$padj < pval_threshold & res_df$log2FoldChange < -log2FC_threshold, "Downregulated", "Not Significant")
)
4. Basic Volcano Plot Using ggplot2
library(ggplot2)
ggplot(res_df, aes(x = log2FoldChange, y = -log10(padj), color = significance)) +
geom_point(alpha = 0.6, size = 1.5) +
scale_color_manual(values = c("blue", "grey", "red")) +
theme_minimal() +
labs(title = "Volcano Plot",
x = "Log2 Fold Change",
y = "-Log10 Adjusted P-Value")
5. Enhanced Volcano Plot Using EnhancedVolcano
for better visualization:
library(EnhancedVolcano)EnhancedVolcano(res,
lab = rownames(res_df),
x = "log2FoldChange",
y = "padj",
xlab = bquote(~Log[2]~Fold~Change),
ylab = bquote(~-Log[10]~P~Value),
pCutoff = 0.05,
FCcutoff = 1.5,
col = c("grey30", "forestgreen", "royalblue", "red2"),
legendPosition = "top",
labSize = 3.0)
6. Save Volcano Plot Export your plot as an image:
ggsave("volcano_plot.png", width = 8, height = 6)
7. Online Tools for Volcano Plot Creation
- EnhancedVolcano: R package for professional volcano plots.
- VolcaNoseR: A web tool to generate volcano plots interactively.
- Bioinformatics.org tools: Various visualization tools, including volcano plots.
Tips for Extracting Filtered Data Use filtering to extract specific genes of interest:
significant_genes <- res_df[res_df$significance != "Not Significant", ]
write.csv(significant_genes, "significant_genes.csv")
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