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MubasherMohammed
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scrna-orchestrator

Automate single-cell RNA-seq analysis with Scanpy, including QC, clustering, and reporting.

Updated May 10, 2026
Search Tags:#data analysis#single-cell RNA-seq#bioinformatics#clustering#Scanpy#marker discovery
One-click install
npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill scrna-orchestrator-mubashermohammed
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scrna-orchestrator
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/scrna-orchestrator
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill scrna-orchestrator-mubashermohammed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scanpy, anndata, scrublet, celltypist, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the analysis of single-cell RNA-seq data, streamlining the entire workflow from data processing to analysis and reporting.

Core Features & Use Cases

  • Data Processing: Perform quality control, normalization, and filtering on single-cell RNA-seq data.
  • Analysis: Run clustering, marker discovery, and doublet detection.
  • Annotation: Annotate cell types using CellTypist.
  • Contrasts: Perform dataset-level and within-cluster contrastive marker analysis.
  • Reporting: Generate comprehensive reports with visualizations and analysis results.
  • Use Case: If you have single-cell RNA-seq data and need to analyze it, this Skill can automate the entire process, saving you time and effort.

Quick Start

Run the scRNA Orchestrator on your single-cell RNA-seq data with the following command:

python scrna_orchestrator.py --input <input.h5ad> --output <report_dir>

Frequently Asked Questions about scrna-orchestrator

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

FAQPage Schema
How do I automate single-cell RNA-seq analysis with Scanpy?▼

You can automate single-cell RNA-seq analysis by running a script that handles data processing, quality control, clustering, marker discovery, annotation, and reporting in one workflow. Provide an input h5ad file to generate comprehensive analysis reports.

What steps are included in an automated scRNA-seq workflow?▼

An automated scRNA-seq workflow includes quality control, normalization, filtering, clustering, doublet detection, cell type annotation, contrastive marker analysis, and final report generation with visualizations.

Does this single-cell RNA-seq workflow support automatic cell type annotation?▼

Yes, the single-cell RNA-seq workflow supports automatic cell type annotation using CellTypist, allowing you to classify cells based on established models alongside clustering and marker discovery.

Do I need AnnData and Scrublet to run single-cell RNA-seq clustering and doublet detection?▼

Yes, you need AnnData, Scrublet, Scanpy, and CellTypist installed. Scrublet handles doublet detection, while AnnData structures the data for clustering and downstream marker discovery.

Can I perform within-cluster contrastive marker analysis on scRNA-seq data?▼

Yes, you can perform both dataset-level and within-cluster contrastive marker analysis on scRNA-seq data to identify differentially expressed genes across specific cell groups.

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