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ChatSpatial

๐Ÿงฌ Analyze spatial transcriptomics data through natural language conversation. Stop writing code, start having conversations with your data. MCP server for Claude Code and Codex.

Description

๐Ÿงฌ Analyze spatial transcriptomics data through natural language conversation. Stop writing code, start having conversations with your data. MCP server for Claude Code and Codex.

README

ChatSpatial

MCP server for spatial transcriptomics analysis via natural language

Paper MLGenX @ ICLR 2026 ENAR 2026 IBC 2026 CI PyPI Python 3.11-3.13 License: MIT Docs

ChatSpatial Overview

ChatSpatial replaces ad-hoc LLM code generation with schema-enforced orchestration. Instead of generating arbitrary scripts, the LLM selects tools and parameters from a curated registry, making spatial transcriptomics workflows more reproducible across sessions and clients.

It exposes 60+ spatial transcriptomics methods as MCP tools, so any MCP-compatible client can analyze data through natural language.


Start Here

  1. Install ChatSpatial โ€” Installation Guide
  2. Configure your MCP client โ€” Configuration Guide
  3. Run your first analysis โ€” Quick Start

Minimal example prompt:

Load /absolute/path/to/spatial_data.h5ad and show me the tissue structure

ChatSpatial works with any MCP-compatible client โ€” Claude Code, Claude Desktop, Codex, OpenCode, and other MCP-capable tools.


Capabilities

60+ methods across 11 categories. Supports 10x Visium, Xenium, Slide-seq v2, MERFISH, seqFISH.

Category Methods
Spatial Domains SpaGCN, STAGATE, GraphST, BANKSY, Leiden, Louvain
Deconvolution FlashDeconv, Cell2location, RCTD, DestVI, Stereoscope, SPOTlight, Tangram, CARD
Cell Communication LIANA+, CellPhoneDB, CellChat (cellchat_r), FastCCC
Cell Type Annotation Tangram, scANVI, CellAssign, mLLMCelltype, scType, SingleR
Differential Expression Wilcoxon, t-test, Logistic Regression, pyDESeq2
Trajectory & Velocity CellRank, Palantir, DPT, scVelo, VeloVI
Spatial Statistics Moran's I, Local Moran, Geary's C, Getis-Ord Gi*, Ripley's K, Co-occurrence, Neighborhood Enrichment, Centrality Scores, Local Join Count, Network Properties
Enrichment GSEA, ORA, Enrichr, ssGSEA, Spatial EnrichMap
Spatial Genes SpatialDE, SPARK-X, FlashS
Integration Harmony, BBKNN, Scanorama, scVI
Other CNV Analysis (InferCNVPy, Numbat), Spatial Registration (PASTE, STalign)

Documentation

Guide Owns
Installation Environment setup, package install, platform notes
Quick Start First successful analysis after setup
Concepts Method selection and analysis reasoning
Examples Prompt recipes and workflow examples
Configuration Exact MCP client configuration syntax
Troubleshooting Symptom โ†’ fix guidance
Methods Reference Canonical tool parameters and defaults
Full Docs Complete documentation site

Citation

If you use ChatSpatial in your research, please cite:

@article{Yang2026.02.26.708361,
  author = {Yang, Chen and Zhang, Xianyang and Chen, Jun},
  title = {ChatSpatial: Schema-Enforced Agentic Orchestration for Reproducible and Cross-Platform Spatial Transcriptomics},
  elocation-id = {2026.02.26.708361},
  year = {2026},
  doi = {10.64898/2026.02.26.708361},
  publisher = {Cold Spring Harbor Laboratory},
  URL = {https://www.biorxiv.org/content/early/2026/03/01/2026.02.26.708361},
  journal = {bioRxiv}
}

ChatSpatial orchestrates many excellent third-party methods. Please also cite the original tools your analysis used.


Contributing

Documentation improvements, bug reports, and new analysis methods are all welcome. See CONTRIBUTING.md.

MIT License ยท GitHub ยท Issues

Release History

VersionChangesUrgencyDate
v1.1.6## What's Changed * perf: memory/speed consistency improvements across core tools and wrappers by @cafferychen777 in https://github.com/cafferychen777/ChatSpatial/pull/19 **Full Changelog**: https://github.com/cafferychen777/ChatSpatial/commits/v1.1.6Low2/28/2026

Dependencies & License Audit

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