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scrna-orchestrator

Run QC, clustering, marker detection, and differential expression on raw-count AnnData .h5ad files.

1.1k|238|Updated Feb 25, 2026
Search Tags:#clustering#anndata#scanpy#differential-expression#umap#scrna#qc
One-click install
npx skills add https://github.com/ClawBio/ClawBio --skill scrna-orchestrator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scrna-orchestrator
Source: https://github.com/ClawBio/ClawBio/tree/main/skills/scrna-orchestrator
Command: npx skills add https://github.com/ClawBio/ClawBio --skill scrna-orchestrator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, anndata, numpy, pandas, matplotlib, scipy, leidenalg, python-igraph.

What problem does it solve?

Single-cell RNA-seq analysis is easy to misconfigure and hard to reproduce when steps like QC, normalization, clustering, and marker/DE testing are run ad hoc; this Skill provides a single local pipeline that enforces raw-count inputs and consistent defaults to produce reproducible results.

Core Features & Use Cases

  • QC and Filtering: Mitochondrial percent filtering, minimum genes/cells thresholds, and early rejection of processed-like inputs.
  • Preprocessing & Feature Selection: Total-count normalization, log1p transformation, and highly variable gene selection.
  • Embedding, Clustering & Markers: PCA, neighbors graph, UMAP, Leiden clustering, and Wilcoxon-based cluster-vs-rest marker discovery.
  • Optional Two-Group DE and Volcano Plot: Wilcoxon two-group DE on any obs column with optional volcano visualization and reproducibility bundle generation.
  • Use Case: Run end-to-end QC, clustering, and marker discovery on a raw-count AnnData .h5ad file to produce figures, tables, and a reproducible commands/environment bundle for publication or review.

Quick Start

Run standard QC, clustering, marker discovery, and optional two-group differential expression on a raw-count .h5ad file and save the report, figures, and tables to the chosen output directory.

Frequently Asked Questions about scrna-orchestrator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run scRNA clustering and marker detection on a local AnnData h5ad file?▼

To run scRNA clustering, you provide a raw-count AnnData .h5ad file, and the pipeline executes total-count normalization, log1p transformation, HVG selection, PCA, UMAP, Leiden clustering, and Wilcoxon marker detection to output figures and tables.

What is the best way to perform reproducible QC and differential expression for scRNA data?▼

Reproducible scRNA QC and differential expression is achieved by enforcing raw-count inputs and consistent defaults, running mitochondrial percent filtering and Wilcoxon DE testing, then generating a reproducibility bundle with commands and environment details.

Can I use processed single-cell RNA-seq matrices for Scanpy clustering analysis?▼

You cannot use processed scRNA matrices because the pipeline validates inputs to reject processed-like data, requiring raw-count matrices to correctly execute total-count normalization, log1p transformation, and downstream clustering.

How does Wilcoxon differential expression work for identifying cluster markers in scRNA data?▼

Wilcoxon differential expression for scRNA marker discovery works by performing cluster-vs-rest testing on normalized data, optionally generating volcano plots for two-group DE comparisons across any obs column in the AnnData object.

Do I need to install Leidenalg and python-igraph to run UMAP and clustering on scRNA data?▼

You need Leidenalg and python-igraph installed alongside Scanpy and Anndata to execute the Leiden clustering, construct the neighbors graph, and generate UMAP embeddings for single-cell RNA-seq data.

Why does my scRNA pipeline fail during normalization and HVG selection?▼

Your scRNA pipeline may fail during normalization if the input is a processed-like matrix, as the pipeline validates inputs to reject processed data and requires raw-count AnnData files for total-count normalization and HVG selection.

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