bioinformatics-statistics

Creating a Volcano Plot from DESeq2 Analysis

January 2, 2025 Off By admin
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Step-by-Step Instructions

1. Install Required Packages Ensure the following R packages are installed:

R
install.packages("ggplot2")
install.packages("ggrepel")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
BiocManager::install("EnhancedVolcano")

2. Perform Differential Expression Analysis Run DESeq2 for your dataset:

R
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):

R
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

R
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:

R
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:

R
ggsave("volcano_plot.png", width = 8, height = 6)

7. Online Tools for Volcano Plot Creation

Tips for Extracting Filtered Data Use filtering to extract specific genes of interest:

R
significant_genes <- res_df[res_df$significance != "Not Significant", ]
write.csv(significant_genes, "significant_genes.csv")

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