deseq2 — Differential Expression Analysis

Run DESeq2 on a raw count matrix to identify differentially expressed genes between conditions.

Capabilities

Task	Input	Output
Two-group DE	counts.csv + metadata.csv	deseq2_results.csv, MA_plot.png
Multi-factor	counts.csv + metadata.csv (multiple cols)	deseq2_results.csv per contrast
Shrinkage	lfcShrink via apeglm	shrunken_results.csv

Requirements

R ≥ 4.3 with DESeq2, apeglm, ggplot2 installed
Input: raw integer counts (not normalized TPM/FPKM)

When to use

Bulk RNA-seq differential expression
ATAC-seq peak differential accessibility (with raw counts)
Spatial transcriptomics DE between regions

python3 run_deseq2.py \
  --counts counts_matrix.csv \
  --metadata sample_metadata.csv \
  --contrast "treatment:drug_vs_control" \
  --output results/deseq2/

Deseq2 | Skills Pool

Deseq2

Deseq2

deseq2 — Differential Expression Analysis

Capabilities

Requirements

When to use

When NOT to use

Example

Deep Research

Data Analyst

Academic Researcher

Data Scientist

Biopython

Binary Analysis Patterns