BIONODE Open Access Tools

Function

DESeq2 is one of the most widely used Bioconductor packages for differential gene expression analysis from RNA-Seq count data. It models counts using the negative binomial distribution and applies shrinkage estimation to improve fold-change and dispersion estimates.

Input Format

• Counts Matrix (.csv): Raw integer count matrix with genes × samples.
• Sample Metadata (.csv): Contains sample information and experimental conditions.
• Design Formula: Example: ~ condition or ~ batch + condition.
• Contrast Factor: Metadata column defining groups.
• Transformation: VST, rlog, or normalized counts.

Output Format

• Differential Expression Results Table (.tsv)
• Significant Genes Table (.tsv)
• Normalized Counts Matrix (.tsv)
• Transformed Matrix (.tsv)
• PCA Plot
• MA Plot
• Volcano Plot
• Dispersion Plot
• Heatmap and Sample Distance Matrix

Applications

• Disease vs Control Expression Analysis
• Drug Response Studies
• Biomarker Discovery
• Pathway and Functional Enrichment Analysis
• Transcriptomics Research
• Cross-sample Quality Control and PCA Analysis

Example Usage

library(DESeq2)

counts <- read.csv("counts.csv", row.names = 1)
coldata <- read.csv("metadata.csv", row.names = 1)

dds <- DESeqDataSetFromMatrix(
    countData = counts,
    colData = coldata,
    design = ~ condition
)

dds <- DESeq(dds)

res <- results(
    dds,
    contrast = c(
        "condition",
        "treated",
        "control"
    )
)

vsd <- vst(dds)

Suggested Reading

• DESeq2 Bioconductor Package
• DESeq2 User Guide
• tximport Package

Citation

Love MI, Huber W, Anders S. (2014).
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
Genome Biology, 15(12):550.
doi:10.1186/s13059-014-0550-8