# 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
