Scrna Orchestrator

---
name: scrna-orchestrator
description: Local Scanpy pipeline for single-cell RNA-seq QC, optional doublet detection, clustering, marker discovery, optional
  CellTypist annotation, optional latent downstream mode from integrated.h5ad/X_scvi, and optional dataset-level plus within-cluster
  contrastive marker analysis from raw-count .h5ad or 10x Matrix Market input.
license: MIT
metadata:
  version: 0.1.0
  author: Yonghao Zhao
  tags:
  - scrna
  - single-cell
  - scanpy
  - clustering
  - differential-expression
  - h5ad
  - mtx
  - 10x
  openclaw:
    requires:
      bins:
      - python3
    always: false
    emoji: 🦖
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    install:
    - kind: uv
      package: scanpy
    - kind: uv
      package: anndata
    - kind: uv
      package: scrublet
    - kind: uv
      package: celltypist
    trigger_keywords:
    - scrna
    - single-cell
    - scanpy
    - h5ad
    - mtx
    - 10x
    - leiden
    - marker genes
    - differential expression
    - contrastive markers
    - integrated.h5ad
    - x_scvi
    - doublet
    - celltypist
---

# 🦖 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, marker analysis, and latent downstream interpretation with inconsistent defaults.
- **With it**: One command produces a consistent `report.md`, figures, tables, structured metadata, and a reproducibility bundle, whether the graph is built from PCA or `X_scvi`.
- **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. **Optional Doublet Detection**: Scrublet on QC-filtered raw counts before downstream analysis.
3. **Preprocessing**: Library-size normalization, `log1p`, and HVG selection.
4. **Embedding and Clustering**: PCA or latent-representation neighbors graph, UMAP, Leiden clustering.
5. **Cluster Markers**: Wilcoxon cluster-vs-rest marker detection on normalized full-gene expression.
6. **Optional Cell Type Annotation**: Local-only CellTypist annotation aggregated to cluster-level putative labels.
7. **Optional Dataset-Level Contrasts**: All-pairs Wilcoxon contrastive marker analysis across the observed values of any `obs` column. **Exploratory only** — see Safety.
8. **Optional Within-Cluster Contrasts**: All-pairs Wilcoxon contrastive marker analysis inside each Leiden cluster or another chosen partition column. **Exploratory only** — see Safety.
9. **Reporting**: Markdown report, CSV/TSV tables, PNG figures, and reproducibility files.

## Input Formats

| Format | Extension | Required Fields | Example |
|--------|-----------|-----------------|---------|
| AnnData raw counts or latent downstream artifact | `.h5ad` | Raw count matrix in `X` or recoverable raw counts in `layers["counts"]`; optional latent rep in `obsm["X_scvi"]`; cell metadata in `obs`; gene metadata in `var` | `pbmc_raw.h5ad`, `integrated.h5ad` |
| 10x Matrix Market | directory, `.mtx`, `.mtx.gz` | `matrix.mtx(.gz)` plus matching `barcodes.tsv(.gz)` and `features.tsv(.gz)` or `genes.tsv(.gz)` | `filtered_feature_bc_matrix/` |
| Demo mode | n/a | none | `python clawbio.py run scrna --demo` |

Notes:
- Processed/normalized/scaled `.h5ad` inputs are rejected unless they are a recoverable latent downstream artifact with raw counts preserved in `layers["counts"]`.
- 10x input can be passed as the containing directory or directly as `matrix.mtx(.gz)`.
- `pbmc3k_processed`-style inputs are out of scope for this skill.

## Workflow

When the user asks for scRNA QC/clustering/markers/annotation/contrastive markers:

1. **Validate**: Check raw-count `.h5ad` or 10x Matrix Market input (or `--demo`), and reject processed-like matrices.
2. **Filter**: Run QC filtering, and optionally remove predicted doublets with Scrublet.
3. **Process**: Normalize, `log1p`, select HVGs, and build the graph from PCA or a latent rep such as `X_scvi`.
4. **Analyze**:
- Always run cluster marker analysis (`leiden`, Wilcoxon).
- Optionally run CellTypist on the normalized full-gene matrix.
- Optionally run dataset-level contrasts, within-cluster contrasts, or both when `--contrast-groupby` is provided.
5. **Generate**: Write `report.md`, `result.json`, tables, figures, and reproducibility bundle.

## CLI Reference

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

# 10x Matrix Market directory
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <filtered_feature_bc_matrix_dir> --output <report_dir>

# Direct matrix.mtx(.gz) path
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <matrix.mtx.gz> --output <report_dir>


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

# Optional doublet detection
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --doublet-method scrublet

# Optional CellTypist annotation
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --annotate celltypist --annotation-model Immune_All_Low

# Optional dataset-level pairwise contrasts
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --contrast-groupby <obs_column> --contrast-scope dataset

# Optional dataset-level + within-cluster contrasts together
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <input.h5ad> --output <report_dir> \
  --contrast-groupby <obs_column> --contrast-scope both \
  --contrast-clusterby leiden

# Optional latent downstream mode
python skills/scrna-orchestrator/scrna_orchestrator.py \
  --input <integrated.h5ad> --output <report_dir> \
  --use-rep X_scvi

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

## Demo

```bash
python clawbio.py run scrna --demo
python clawbio.py run scrna --demo --doublet-method scrublet
```

Expected output:
- `report.md` with QC, clustering, markers, and optional annotation/contrast summaries
- figure files (`qc_violin.png`, `umap_leiden.png`, `marker_dotplot.png`)
- marker, doublet, annotation, dataset-level contrast, and within-cluster contrast tables when enabled
- 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. **Optional doublet detection**:
- `scanpy.pp.scrublet` on QC-filtered raw counts
- Remove predicted doublets before normalization and clustering
3. **Preprocess**:
- Normalize total counts to `1e4`
- Apply `log1p`
- Select HVGs (`flavor="seurat"`)
4. **Embed and cluster**:
- Scale (`max_value=10`) on the HVG branch
- PCA, neighbors graph, UMAP
- Leiden clustering
5. **Markers**:
- `scanpy.tl.rank_genes_groups(groupby="leiden", method="wilcoxon", pts=True)`
6. **Optional annotation**:
- Run local CellTypist on normalized/log1p full-gene expression
- Aggregate per-cell predictions to cluster-level majority labels with support and confidence
7. **Optional dataset-level contrasts**:
- For every unordered pair of observed groups in `--contrast-groupby`, run `scanpy.tl.rank_genes_groups(..., groups=[group1], reference=group2, method="wilcoxon", pts=True)`
- Export full statistics and top genes by score per pairwise comparison
8. **Optional within-cluster contrasts**:
- For every cluster in `--contrast-clusterby` and every unordered pair of observed groups in `--contrast-groupby`, run the same Wilcoxon contrast on the cluster subset
- Skip cluster/comparison pairs where either side has fewer than 2 cells, and report the skipped count

## Example Queries

- "Run standard QC and clustering on my h5ad file"
- "Cluster my 10x matrix.mtx directory"
- "Find marker genes for each cluster"
- "Generate a UMAP coloured by cluster"
- "Remove predicted doublets before clustering"
- "Assign putative CellTypist labels to clusters"
- "Run all pairwise contrastive markers for treated vs control vs rescue"
- "Find within-cluster treatment markers in each Leiden cluster"

## Output Structure

```text
output_directory/
├── report.md
├── result.json
├── figures/
│   ├── qc_violin.png
│   ├── umap_leiden.png
│   └── marker_dotplot.png
├── tables/
│   ├── cluster_summary.csv
│   ├── markers_top.csv
│   ├── markers_top.tsv
│   ├── doublet_summary.csv      # only when doublet detection is enabled
│   ├── cluster_annotations.csv  # only when annotation is enabled
│   ├── contrastive_markers_full.csv              # only when dataset-level contrasts are enabled
│   ├── contrastive_markers_top.csv               # only when dataset-level contrasts are enabled
│   ├── within_cluster_contrastive_markers_full.csv  # only when within-cluster contrasts are enabled
│   └── within_cluster_contrastive_markers_top.csv   # only when within-cluster contrasts are enabled
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256
```

## Dependencies

**Required**:
- `scanpy` >= 1.10
- `anndata` >= 0.10
- `scipy`
- `numpy`, `pandas`, `matplotlib`, `leidenalg`, `python-igraph`

**Optional**:
- `scrublet` for `--doublet-method scrublet`
- `celltypist` for `--annotate celltypist`

**Out of scope**:
- `scvi-tools` / `scANVI`

## 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.
- **Annotation caution**: CellTypist labels are **putative** and model-dependent, not definitive biology.
- **Contrast caution**: `--contrast-groupby` runs a per-**cell** Wilcoxon test. When the column is a biological condition (treated/control, genotype, timepoint), cells from one donor are not independent replicates, so the exported `pvals_adj` reflects cell count rather than biological replication. Treat `contrastive_markers_*.csv` as a ranked exploratory list, never as condition-level differential expression. For a defensible condition contrast, aggregate to pseudobulk per donor-sample per cell type (`scanpy.get.aggregate` by sample × cell type) and run `pydeseq2`, DESeq2, edgeR, or limma-voom with donor as a blocking factor — see the `pydeseq2` and `single-cell-biologist` skills.
- **Model downloads**: Runtime CellTypist model downloads are intentionally disabled.
- **Reproducibility**: Writes command/environment/checksum bundle.

## Integration with Bio Orchestrator

**Trigger conditions**:
- File extension `.h5ad`, `.mtx`, or `.mtx.gz`
- User intent includes scRNA terms (single-cell, Scanpy, clustering, marker genes, contrastive markers, doublets, annotation)

**Current limitations**:
- Raw-count `.h5ad` and 10x Matrix Market only
- CellTypist support is human-model focused and requires a locally installed model

## Status

**MVP implemented** -- supports `.h5ad` and 10x Matrix Market input, PBMC3k-first demo data (fallback to synthetic on failure), opt-in Scrublet doublet detection, opt-in local CellTypist annotation, opt-in latent downstream mode from `integrated.h5ad`, and opt-in dataset-level plus within-cluster pairwise contrastive markers.

## Citations

- [Scanpy documentation](https://scanpy.readthedocs.io/) — analysis API and methods.
- [AnnData documentation](https://anndata.readthedocs.io/) — data model.
- [Leiden algorithm paper](https://www.nature.com/articles/s41598-019-41695-z) — community detection.
- [Scrublet paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1736-8) — computational doublet detection.
- [CellTypist documentation](https://www.celltypist.org/) — model-based immune and general cell annotation.