Chatspatial
@cafferychen777
Chatspatial について
Natural language-driven spatial transcriptomics analysis via MCP. Integrates 60+ analytical methods across 15 categories including preprocessing, visualization, spatial statistics, cell communication, deconvolution, and trajectory analysis.
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"ChatSpatial": {
"command": "python",
"args": [
"--version",
"#",
"Should",
"be",
"3.10+"
]
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Chatspatial?
Chatspatial is an agentic workflow orchestration platform for spatial transcriptomics analysis. It integrates 60+ state-of-the-art methods from fragmented Python and R ecosystems into a unified conversational interface. Built on the Model Context Protocol (MCP), it enables human-steered discovery through natural language in Claude Desktop or Claude Code, eliminating manual data conversion and complex programming.
How to use Chatspatial?
Create a Python 3.10+ virtual environment, install the package with pip install -e ".[full]", then configure Claude Desktop by adding an MCP server entry to claude_desktop_config.json or Claude Code with the claude mcp add command. Download sample data and load it using absolute paths (e.g., /Users/yourname/Downloads/card_spatial.h5ad). After setup, simply type natural language requests in Claude chat to analyze spatial transcriptomics data—no coding required.
Key features of Chatspatial
- 75+ analysis methods across 12 categories (cell annotation, spatial domains, deconvolution, etc.)
- Supports 10x Genomics Visium, Xenium, Slide-seq v2, MERFISH, seqFISH, and standard formats (H5AD, MTX, CSV)
- Natural language interface for data loading, analysis, and visualization
- Publication-ready visualizations (spatial plots, heatmaps, communication networks)
- Optional GPU acceleration for deep learning methods
- MIT licensed for academic and commercial use
Use cases of Chatspatial
- Load a 10x Visium dataset and identify spatial domains using SpaGCN
- Find marker genes for a specific domain and generate expression heatmaps
- Deconvolve spatial data with a reference using Cell2location or RCTD
- Analyze cell–cell communication between spatial regions via LIANA+ and CellPhoneDB
- Detect spatially variable genes
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