Infercnv-MCP
About
Infer Copy Number Variations (CNVs) from single-cell RNA-Seq data using a natural language interface.
Details
- Author
- scmcphub
- GitHub stars
- 2
- Downloads
- 317
- Categories
- Developer Tools, Other
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- Natural language interface for CNV analysis
- IO module: read/write scRNA-Seq data and load gene positions
- Preprocessing module: neighbors computation and data preparation
- Tool module: CNV inference and CNV scoring
- Plotting module: chromosome heatmaps, UMAP, and t‑SNE visualizations
- Supports both local (stdio) and remote (shttp) transports
Install the package from PyPI (pip install infercnv-mcp), then run it either locally (infercnv-mcp run) or as a remote HTTP server (infercnv-mcp run --transport shttp --port 8000). Configure your MCP client (e.g., Cherry Studio, Cline, Agno) with the appropriate command or endpoint.
Natural language interface for Copy Number Variation (CNV) inference from scRNA-Seq data with infercnvpy through MCP.
- IO module for reading and writing scRNA-Seq data, load gene position
- Preprocessing module for neighbors computation and data preparation
- Tool module for CNV inference, cnv score
- Plotting module for chromosome heatmaps, UMAP, and t-SNE visualizations
- Researchers who want to infer CNVs from scRNA-Seq data using natural language
- Agent developers who want to integrate CNV analysis into their applications
You can use infercnv-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
scmcphub's complete documentation is available athttps://docs.scmcphub.org
Refer to the following configuration in your MCP client:
$ which infercnv /home/test/bin/infercnv-mcp
"mcpServers": { "infercnv-mcp": { "command": "/home/test/bin/infercnv-mcp", "args": [ "run" ] } }
Refer to the following configuration in your MCP client:
infercnv-mcp run --transport shttp --port 8000
Then configure your MCP client, like this:
If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome!
If you use infercnv-mcp in your research, please consider citing following work:
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