rnaseq-de

Research

Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.

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🧬 RNA-seq Differential Expression

This skill performs differential expression on bulk RNA-seq or pseudo-bulk count matrices.

Core Capabilities

Input validation for count matrix and sample metadata
Pre-DE QC (library size, detected genes, low-count filtering)
PCA visualisation on normalized expression
Differential expression from formula + contrast
Volcano and MA plots
Markdown report with reproducibility files

Input Contract

Count matrix (.csv or .tsv): rows are genes, columns are samples, first column is gene identifier
Metadata table (.csv or .tsv): one row per sample, must include sample_id
Formula: e.g. ~ condition or ~ batch + condition
Contrast: factor,numerator,denominator (e.g. condition,treated,control)

Output Structure

rnaseq_de_report/
├── report.md
├── figures/
│   ├── pca.png
│   ├── volcano.png
│   └── ma_plot.png
├── tables/
│   ├── qc_summary.csv
│   ├── normalized_counts.csv
│   └── de_results.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Usage

python rnaseq_de.py \
  --counts counts.csv \
  --metadata metadata.csv \
  --formula "~ batch + condition" \
  --contrast "condition,treated,control" \
  --output report_dir

Safety

Local-only processing
Warn before overwriting existing output
Report-level disclaimer required