Infercnv-MCP
Infer Copy Number Variations (CNVs) from single-cell RNA-Seq data using a natural language interface.Read full description
scmcphubLABSCO SUMMARY
The whole pipeline, not just the fate mapping.
Wrapping preprocessing and plotting alongside the analysis step is what makes this usable end to end — a clustering call is worth little if getting the data normalised still means dropping back to a notebook. The remote transport is the practical detail for this field: single-cell work happens on machines with the memory for it, and this lets the client stay local while the computation does not.
What it is
A natural-language interface to CellRank for scRNA-seq work. It spans the analysis pipeline rather than one step: reading and writing data, preprocessing, the analysis tools, and plotting — usable from AI clients, editor plugins and agent frameworks alike.
What you get
- An IO module for reading and writing scRNA-seq data
- A preprocessing module covering filtering, quality control, normalization, scaling, highly-variable gene selection, PCA and neighbours
- An analysis module including clustering and differential expression
- A plotting module for violin plots, heatmaps and dotplots
- Two transports: stdio for a local client, or Streamable HTTP when the analysis runs on a server and the client is on your laptop
Requirements
pip install cellrank-mcp from PyPI, then cellrank-mcp run to check it works. For a local client, point the config at the installed binary with run as its argument — which cellrank-mcp finds the path. To run it remotely, launch cellrank-mcp run --transport shttp --port 8000 on the server and give the client the /mcp URL. Full documentation is at docs.scmcphub.org. If you use it in research, the README asks you to cite the CellRank 2 paper in Nature Methods.
Setup effort
One command — pip install cellrank-mcp
USE CASES
01
Running a clustering analysis by describing it
Read the dataset, preprocess it and cluster the cells through conversation rather than assembling the notebook first.
02
Getting data ready for analysis
Filter, run quality control, normalize, scale, select highly-variable genes and compute PCA and neighbours in sequence.
03
Looking at the result
Produce violin plots, heatmaps or dotplots for the genes and groups you want to inspect.
04
Analysing on a server from your laptop
Run the server over Streamable HTTP where the data and memory are, and connect the client to its URL.
BEFORE YOU INSTALL
What to know first
Requires
pip on PATH
INSTALLATION
Connect your client
Add the block to your client, or run the command directly.
Server setup
Configuration file
JSON
{
"mcpServers": {
"cellrank-mcp": {
"command": "cellrank-mcp",
"args": [
"run"
]
}
}
}If your client cannot find cellrank-mcp, put the full path printed by which cellrank-mcp in "command"
pip
TERMINAL
pip install cellrank-mcp
