chatspatial 1.2.8

ChatSpatial: Natural language-driven spatial transcriptomics analysis via Model Context Protocol (MCP) integration

ChatSpatial

MCP server for spatial transcriptomics analysis via natural language

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.

ChatSpatial exposes 20 schema-validated MCP tools that orchestrate 65 spatial transcriptomics methods across 15 analytical categories. The tools are the stable natural-language interface; the methods are the analysis backends selected through tool parameters.

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Start Here

1. Install ChatSpatial — Installation Guide for Python/uv setup, or Docker Guide for the GHCR image
2. Configure your MCP client — Configuration Guide
3. Run your first analysis — Quick Start

Docker quick start:

docker pull ghcr.io/cafferychen777/chatspatial:v1.2.7

Minimal example prompt:

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

If you use Docker, mount host data to /data and prompt with the container path, for example /data/spatial_data.h5ad.

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

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Capabilities

Current coverage includes 65 methods across 15 analytical categories, exposed through 20 MCP tools. Supports 10x Visium, Xenium, Slide-seq v2, MERFISH, seqFISH.

Category	Example methods
Data Loading & Preprocessing	Scanpy I/O, QC, Normalization, HVG, PCA, Neighbors
Visualization	Spatial plots, Embedding plots, Gene expression overlays
Spatial Domain Identification	SpaGCN, STAGATE, GraphST, BANKSY, Leiden, Louvain
Deconvolution	FlashDeconv, Cell2location, RCTD, DestVI, Stereoscope, SPOTlight, Tangram, CARD
Cell-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 Inference	CellRank, Palantir, DPT
RNA Velocity	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 Analysis	GSEA, ORA, Enrichr, ssGSEA, Spatial EnrichMap
Spatially Variable Genes	SpatialDE, SPARK-X, FlashS
Multi-sample Integration	Harmony, BBKNN, Scanorama, scVI
CNV Analysis	InferCNVPy, Numbat
Spatial Registration	PASTE, STalign

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Documentation

Guide	Use this when...
Installation	You need to install ChatSpatial in a Python environment
Docker	You want a reproducible container runtime or local dependency resolution fails
Configuration	You need exact MCP client syntax or the runtime path model
Quick Start	ChatSpatial is installed and you want the first successful analysis
Concepts	You need to choose an analysis strategy from a biological question
Examples	You want copy-pasteable natural-language workflow prompts
Methods Reference	You need canonical tool names, method names, parameters, and defaults
Troubleshooting	Setup, data loading, or analysis behavior is not working
Full Docs	You want the complete documentation site

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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.

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Contributing

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

MIT License · GitHub · Issues