Tooluniverse Spatial Transcriptomics

Handles the full pipeline for spatial transcriptomics data from platforms like 10x Visium, MERFISH, and Slide-seq. You get quality control, spatial clustering to identify tissue domains, detection of spatially variable genes using Moran's I, and cell-cell interaction mapping through ligand-receptor databases. The integration piece is solid: it can deconvolve cell types from scRNA-seq references using Cell2location or Tangram, then map those annotations onto tissue architecture. Built on squidpy and scanpy, so if you're already in that ecosystem it's a natural fit. The workflow is comprehensive but you'll need local Python for the heavy computation, while gene context and L-R databases hit ToolUniverse APIs.

skills.sh installs: 411
GitHub stars: 1.8K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-spatial-transcriptomics --agent claude-code

Installs into .claude/skills of the current project.

Files

Spatial Transcriptomics Analysis

Comprehensive analysis of spatially-resolved transcriptomics data to understand gene expression patterns in tissue architecture context. Combines expression profiling with spatial coordinates to reveal tissue organization, cell-cell interactions, and spatially variable genes.

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.