decoupler-mcp

# 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 ## 📚 Documentation scmcphub's complete documentation is available at https://docs.scmcphub.org ## 🏎️ Quickstart ### Install Install from PyPI ``` pip install decoupler-mcp ``` you can test it by running ``` decoupler-mcp run ``` #### run decoupler-mcp locally Refer to the following configuration in your MCP client: check path ``` $ which decoupler /home/test/bin/decoupler-mcp ``` ``` "mcpServers": { "decoupler-mcp": { "command": "/home/test/bin/decoupler-mcp", "args": [ "run" ] } } ``` #### run decoupler-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 in local AI client, like this: ``` "mcpServers": { "decoupler-mcp": { "url": "http://localhost:8000/mcp" } } ``` ## 🤝 Contributing If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). 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