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Chatspatial MCP Server

@cafferychen777

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

cafferychen777

提交者

Chen Yang

关于 Chatspatial MCP Server

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.

#spatial-transcriptomics#bioinformatics#single-cell#scanpy#squidpy

Chatspatial MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "ChatSpatial": {
      "command": "python",
      "args": [
        "--version",
        "#",
        "Should",
        "be",
        "3.10+"
      ]
    }
  }
}

Chatspatial MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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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