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 two-group contrastive marker analysis from raw-count .h5ad or 10x Matrix Market input.
version	0.1.0
author	Yonghao Zhao
license	MIT
tags	["scrna","single-cell","scanpy","clustering","differential-expression","h5ad","mtx","10x"]
metadata	{"openclaw":{"requires":{"bins":["python3"],"env":[],"config":[]},"always":false,"emoji":"🦖","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux"],"install":[{"kind":"uv","package":"scanpy","bins":[]},{"kind":"uv","package":"anndata","bins":[]},{"kind":"uv","package":"scrublet","bins":[]},{"kind":"uv","package":"celltypist","bins":[]}],"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 Contrastive Markers: Two-group Wilcoxon contrastive marker analysis on any obs column.
8. Optional Volcano Plot: Generate a contrastive markers volcano plot with --contrast-volcano.
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,