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RNA-seq DE

Run differential expression analysis on bulk or pseudo-bulk RNA-seq count matrices using PyDESeq2. Specify a design formula and contrast to identify differentially expressed genes, generate volcano plots, and export ranked gene lists.

Quick Demo

python3 skills/rnaseq-de/rnaseq_de.py \
  --demo \
  --output /tmp/rnaseq_de_demo

CLI Reference

# Standard DE analysis
python3 skills/rnaseq-de/rnaseq_de.py \
  --counts <counts.csv> \
  --metadata <metadata.csv> \
  --formula "~ batch + condition" \
  --contrast "condition,treated,control" \
  --output <report_dir>

# Demo mode
python3 skills/rnaseq-de/rnaseq_de.py \
  --demo \
  --output /tmp/rnaseq_de_demo
Argument Required Description
--counts Yes* Path to gene count matrix (CSV or TSV, genes as rows)
--metadata Yes* Path to sample metadata (CSV or TSV)
--formula Yes* DESeq2-style design formula (e.g. ~ batch + condition)
--contrast Yes* Contrast specification: factor,level,reference
--output Yes Output directory for report and figures
--demo No Run with built-in synthetic dataset

*Not required when using --demo.

Output

  • report.md -- DE analysis report with summary statistics
  • figures/ -- Volcano plot, MA plot, PCA of samples
  • tables/ -- Full DE results table, significant genes list (CSV)
  • commands.sh -- Reproducibility script
  • checksums.sha256 -- Output verification