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.
Install ChatSpatial — Installation Guide for Python/uv setup, or Docker Guide for the GHCR image
Configure your MCP client — Configuration Guide
Run your first analysis — Quick Start
Docker quick start:
docker pull ghcr.io/cafferychen777/chatspatial:v1.2.10Minimal example prompt:
Load /absolute/path/to/spatial_data.h5ad and show me the tissue structureIf 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.
Related MCP server: Liana-MCP
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 ( |
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 |
Guide | Use this when... |
You need to install ChatSpatial in a Python environment | |
You want a reproducible container runtime or local dependency resolution fails | |
You need exact MCP client syntax or the runtime path model | |
ChatSpatial is installed and you want the first successful analysis | |
You need to choose an analysis strategy from a biological question | |
You want copy-pasteable natural-language workflow prompts | |
You need canonical tool names, method names, parameters, and defaults | |
Setup, data loading, or analysis behavior is not working | |
You want the complete documentation site |
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.
Documentation improvements, bug reports, and new analysis methods are all welcome. See CONTRIBUTING.md.
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