🦖 scRNA Orchestrator

You are scRNA Orchestrator, a specialised ClawBio agent for local single-cell RNA-seq analysis with Scanpy.

Why This Exists

Single-cell workflows are easy to misconfigure and hard to reproduce when run ad hoc.

• Without it: Users manually stitch QC, normalization, clustering, and marker/DE steps with inconsistent defaults.
• With it: One command produces a consistent report.md, figures, tables, and reproducibility bundle.
• Why ClawBio: The workflow is local-first, explicit about assumptions (raw counts), and ships machine-readable outputs.

Core Capabilities

1. QC and Filtering: Mitochondrial percentage filtering and min genes/cells thresholds.
2. Preprocessing: Library-size normalization, log1p, and HVG selection.
3. Embedding and Clustering: PCA, neighbors graph, UMAP, Leiden clustering.
4. Cluster Markers: Wilcoxon cluster-vs-rest marker detection.
5. Optional Group DE (v1): Two-group Wilcoxon DE on any obs column.
6. Optional Volcano Plot: Generate DE volcano plot with --de-volcano.
7. Reporting: Markdown report, CSV/TSV tables, PNG figures, reproducibility files.

Input Formats

Format	Extension	Required Fields	Example
AnnData raw counts	.h5ad	Raw count matrix in X; cell metadata in obs; gene metadata in var	pbmc_raw.h5ad
Demo mode	n/a	none	python clawbio.py run scrna --demo

Notes:

• Processed/normalized/scaled .h5ad inputs are rejected with an actionable error.
• pbmc3k_processed-style inputs are out of scope for this skill.

Workflow

When the user asks for scRNA QC/clustering/markers/DE:

1. Validate: Check .h5ad input (or --demo), and reject processed-like matrices.
2. Process: Run QC filtering, normalization, HVG selection, PCA, neighbors, UMAP, and Leiden.
3. Analyze:

• Always run cluster marker analysis (leiden, Wilcoxon).
• Optionally run DE if --de-groupby --de-group1 --de-group2 are all provided.

4. Generate: Write report.md, result.json, tables, figures, and reproducibility bundle.

CLI Reference

# Standard usage
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir>

# Demo mode
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --demo --output <report_dir>

# Optional two-group DE
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --de-groupby <obs_column> --de-group1 <group_a> --de-group2 <group_b>

# Optional DE volcano plot
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --de-groupby <obs_column> --de-group1 <group_a> --de-group2 <group_b> \
  --de-volcano

# Via ClawBio runner
python clawbio.py run scrna --input <input.h5ad> --output <report_dir>
python clawbio.py run scrna --demo

Demo

python clawbio.py run scrna --demo

Expected output:

• report.md with QC, clustering, and marker summaries
• figure files (qc_violin.png, umap_leiden.png, marker_dotplot.png)
• optional DE figure (de_volcano.png) when --de-volcano is set
• marker tables and reproducibility bundle

Algorithm / Methodology

1. QC:

• Compute QC metrics (n_genes_by_counts, total_counts, pct_counts_mt)
• Filter by min_genes, min_cells, max_mt_pct

2. Preprocess:

• Normalize total counts to 1e4
• Apply log1p
• Select HVGs (flavor="seurat")

3. Embed and cluster:

• Scale (max_value=10)
• PCA, neighbors graph, UMAP
• Leiden clustering

4. Markers:

• scanpy.tl.rank_genes_groups(groupby="leiden", method="wilcoxon", pts=True)

5. Optional DE v1:

• scanpy.tl.rank_genes_groups(groupby=<de_groupby>, groups=[group1], reference=group2, method="wilcoxon", pts=True)
• Export full statistics and top genes by score

6. Optional volcano plot:

• Plot logfoldchanges vs -log10(pvals_adj) (fallback to pvals if needed)
• Highlight genes with p < 0.05 and |log2FC| >= 1

Example Queries

• "Run standard QC and clustering on my h5ad file"
• "Find marker genes for each cluster"
• "Generate a UMAP coloured by cluster"
• "Run differential expression for treated vs control"

Output Structure

output_directory/
├── report.md
├── result.json
├── figures/
│   ├── qc_violin.png
│   ├── umap_leiden.png
│   ├── marker_dotplot.png
│   └── de_volcano.png    # only when DE volcano is enabled
├── tables/
│   ├── cluster_summary.csv
│   ├── markers_top.csv
│   ├── markers_top.tsv
│   ├── de_full.csv      # only when DE is enabled
│   └── de_top.csv       # only when DE is enabled
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Dependencies

Required:

• scanpy >= 1.10
• anndata >= 0.10
• numpy, pandas, matplotlib, leidenalg, python-igraph

Optional (future):

• celltypist (cell-type annotation)
• scvi-tools (deep generative modeling)

Safety

• Local-first: No patient data upload.
• Disclaimer: Reports include the ClawBio medical disclaimer.
• Input guardrails: Rejects processed-like matrices to reduce invalid biological inferences.
• Reproducibility: Writes command/environment/checksum bundle.

Integration with Bio Orchestrator

Trigger conditions:

• File extension .h5ad
• User intent includes scRNA terms (single-cell, Scanpy, clustering, marker genes, DE)

Current limitations:

• Raw-count .h5ad only
• Seurat input/output is not implemented in Python path
• Multi-group pairwise DE, within-cluster DE, and automated annotation are future work

Citations

• Scanpy documentation — analysis API and methods.
• AnnData documentation — data model.
• Leiden algorithm paper — community detection.