CellTypist

An MCP (Model Context Protocol) server for automated cell type annotation in scRNA-seq analysis using CellTypist with natural language!

🎯 What can it do?

• Automatic cell type annotation using pre-trained CellTypist models
• List available models with descriptions and metadata
• Download models from CellTypist repository
• Train custom models on your own annotated data
• Extract marker genes for specific cell types
• Majority voting for robust predictions based on local subclusters
• Visualization with dotplot comparing predictions to reference labels

🧬 About CellTypist

CellTypist is an automated cell type annotation tool for scRNA-seq datasets based on logistic regression classifiers. It provides:

• Fast and accurate predictions using regularized linear models
• Pre-trained models for various tissues and cell types
• Majority voting approach to refine predictions
• Custom model training capabilities

📦 Installation

From source

git clone <repository-url>
cd celltypist-mcp
pip install -e .

🚀 Quick Start

Run locally with stdio transport

celltypist-mcp run

Run with a pre-loaded dataset

celltypist-mcp run --data /path/to/your/data.h5ad

Run with SSE transport (for remote access)

celltypist-mcp run --transport sse --port 8000 --host 0.0.0.0

🔧 Configuration

For AI Clients (e.g., Claude Desktop, Cherry Studio)

Add to your MCP client configuration:

{
  "mcpServers": {
    "celltypist": {
      "command": "celltypist-mcp",
      "args": ["run"]
    }
  }
}

With pre-loaded data

{
  "mcpServers": {
    "celltypist": {
      "command": "celltypist-mcp",
      "args": ["run", "--data", "/path/to/your/data.h5ad"]
    }
  }
}

Remote SSE connection

First, run the server on your machine:

celltypist-mcp run --transport sse --port 8000

Then configure your MCP client:

http://localhost:8000/sse

🛠️ Available Tools

1. celltypist_list_models

List all available CellTypist models with descriptions.

Example usage:

"Show me available CellTypist models"
"List all immune cell type models"

2. celltypist_annotate

Annotate cell types in your scRNA-seq data.

Parameters:

• model: Model name (e.g., "Immune_All_High.pkl")
• majority_voting: Enable majority voting (default: False)
• over_clustering: Column in adata.obs for clustering (optional)
• mode: "best match" or "prob match" (default: "best match")
• p_thres: Probability threshold for multi-label (default: 0.5)

Example usage:

"Annotate my cells using the Immune_All_High model"
"Run CellTypist with majority voting on my data"
"Use the Immune_All_Low model with leiden clustering for majority voting"

3. celltypist_download_model

Download CellTypist models.

Parameters:

• model: Model name or list of names (None downloads all)
• force_update: Force update to latest version (default: False)

Example usage:

"Download the Immune_All_High model"
"Download all available CellTypist models"
"Update the Immune_All_Low model to the latest version"

4. celltypist_get_model_info

Get detailed information about a specific model.

Parameters:

• model: Model name

Example usage:

"What cell types are in the Immune_All_High model?"
"Show me information about the Immune_All_Low model"
"How many features does the Immune_All_High model use?"

5. celltypist_extract_markers

Extract top marker genes for a specific cell type.

Parameters:

• model: Model name
• cell_type: Cell type name
• top_n: Number of top markers (default: 10)

Example usage:

"What are the top marker genes for T cells in Immune_All_High?"
"Show me 20 marker genes for macrophages"
"Extract markers for B cells from the Immune_All_Low model"

6. celltypist_train

Train a custom CellTypist model.

Parameters:

• labels: Column in adata.obs with cell type labels
• model_name: Filename to save the model
• use_SGD: Use SGD learning for large datasets (default: False)
• C: L2 regularization strength (default: 1.0)
• max_iter: Maximum iterations (optional)
• feature_selection: Enable feature selection (default: False)
• top_genes: Number of top genes to select (default: 300)

Example usage:

"Train a CellTypist model using the 'cell_type' column and save it as 'my_model.pkl'"
"Create a custom model with SGD learning and feature selection"

7. celltypist_dotplot

Generate a dotplot comparing predictions with reference labels.

Parameters:

• use_as_reference: Column in adata.obs with reference labels
• use_as_prediction: "predicted_labels" or "majority_voting" (default: "majority_voting")
• save: Filename to save figure (optional)

Example usage:

"Create a dotplot comparing CellTypist predictions with my cell_type labels"
"Visualize the majority voting results against leiden clusters"
"Generate a dotplot and save it as 'results.png'"

📊 Typical Workflow

1. List available models

   "What CellTypist models are available?"
   

2. Download a model (if not already downloaded)

   "Download the Immune_All_High model"
   

3. Annotate your cells

   "Annotate my cells using Immune_All_High with majority voting"
   

4. Visualize results

   "Create a dotplot comparing predictions with my manual annotations"
   

5. Extract markers (optional)

   "What are the marker genes for T cells in this model?"
   

🧪 Example Conversations

Example 1: Quick annotation

User: "I have scRNA-seq data loaded. Can you annotate the cell types?"
Assistant: [Lists available models]
User: "Use the Immune_All_High model"
Assistant: [Runs celltypist_annotate and shows results]

Example 2: Custom model training

User: "I want to train my own CellTypist model"
Assistant: "What column contains your cell type labels?"
User: "The 'cell_type' column"
Assistant: [Runs celltypist_train and saves the model]

🔬 Data Requirements

• Input data should be in AnnData format (.h5ad)
• Expression matrix should be log1p normalized to 10,000 counts per cell
• For training: cell type labels should be in adata.obs

📝 Notes

• The first time you use a model, it will be downloaded automatically
• Majority voting requires either an existing clustering or will auto-cluster
• Trained models are saved locally and can be reused
• All results are saved to adata.obs columns prefixed with celltypist_

🔗 Related Projects

• CellTypist - The original CellTypist tool
• MCP - Model Context Protocol
• Scanpy - Single-cell analysis in Python