decoupler-mcp

decoupler-mcp

1

Decoupler-MCP provides a natural language interface for conducting scRNA-Seq analysis through the Model Context Protocol. It supports various features like data reading, pathway inference, clustering, and plotting, making scRNA-Seq analysis more accessible.

decoupler-MCP

Natural language interface for scRNA-Seq analysis with decoupler through MCP.

🪩 What can it do?

  • IO module like read and write scRNA-Seq data
  • Pathway activity/Transcription factor inference
  • Tool module, like clustering, differential expression etc.
  • Plotting module, like violin, umap/tsne

❓ Who is this for?

  • Anyone who wants to do scRNA-Seq analysis natural language!
  • Agent developers who want to call decoupler's functions for their applications

🌐 Where to use it?

You can use decoupler-mcp in most AI clients, plugins, or agent frameworks that support the MCP:

  • AI clients, like Cherry Studio
  • Plugins, like Cline
  • Agent frameworks, like Agno

🎬 Demo

A demo showing scRNA-Seq cell cluster analysis in a AI client Cherry Studio using natural language based on decoupler-mcp

🏎️ Quickstart

Install

Install from PyPI

pip install decoupler-mcp

you can test it by running

decoupler-mcp run
run scnapy-server locally

Refer to the following configuration in your MCP client:

"mcpServers": {
  "decoupler-mcp": {
    "command": "decoupler-mcp",
    "args": [
      "run"
    ]
  }
}
run scnapy-server remotely

Refer to the following configuration in your MCP client:

run it in your server

decoupler-mcp run --transport shttp --port 8000

Then configure your MCP client, like this:

http://localhost:8000/mcp

🤝 Contributing

If you have any questions, welcome to submit an issue, or contact me(). Contributions to the code are also welcome!

Citing

If you use decoupler-mcp in for your research, please consider citing following work:

Badia-i-Mompel P., Vélez Santiago J., Braunger J., Geiss C., Dimitrov D., Müller-Dott S., Taus P., Dugourd A., Holland C.H., Ramirez Flores R.O. and Saez-Rodriguez J. 2022. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbac016