scRNA Orchestrator

End-to-end single-cell RNA-seq pipeline built on Scanpy. QC filtering, normalisation, dimensionality reduction, clustering, marker gene identification, and doublet removal.

Quick Demo

python3 skills/scrna-orchestrator/scrna_orchestrator.py --demo --output /tmp/scrna_demo

CLI Reference

python3 skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <data.h5ad> \
  --output <report_dir>

# With doublet detection
python3 skills/scrna-orchestrator/scrna_orchestrator.py \
  --demo --doublet-method scrublet --output /tmp/scrna_doublet_demo

Argument	Required	Description
--input	Yes*	Path to AnnData (.h5ad) file
--output	Yes	Output directory
--demo	No	Use built-in PBMC3k demo data
--doublet-method	No	Doublet detection method (scrublet)

Pipeline Steps

1. QC Filtering — mitochondrial %, gene counts, cell counts
2. Normalisation — library size normalisation + log1p
3. HVG Selection — highly variable genes
4. PCA — dimensionality reduction
5. Neighbourhood Graph — k-NN graph construction
6. Clustering — Leiden community detection
7. UMAP — 2D embedding visualisation
8. Marker Genes — rank_genes_groups per cluster
9. Doublet Detection — optional Scrublet integration

Output

• report.md — Analysis report with QC stats and cluster annotations
• figures/ — UMAP plots, QC violin plots, marker gene dotplots
• processed.h5ad — Processed AnnData object
• Reproducibility bundle