scientific-spatial-transcriptomics

Analyze spatial transcriptomics data to identify gene expression patterns and tissue domains.

3|1|Updated Feb 11, 2026

Search Tags:#svg #ligand-receptor #deconvolution #spatial-transcriptomics #scanpy #squidpy

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-spatial-transcriptomics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-spatial-transcriptomics
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-spatial-transcriptomics
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-spatial-transcriptomics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end spatial transcriptomics analysis, enabling preprocessing, detection of spatially variable genes, automatic spatial domain identification, cell-type deconvolution, and ligand-receptor proximity analysis within a Squidpy-aligned workflow.

Core Features & Use Cases

Preprocessing of spatial data (quality control, normalization) and integration with common spatial libraries.
Spatial domain discovery and gene pattern detection (Moran's I / SpatialDE / BayesSpace / STAGATE).
Cell-type deconvolution in spatial spots using reference scRNA-seq datasets (cell2location).
Spatial ligand-receptor interaction analysis to study inter-domain signaling.
Use Case: Researchers analyzing Visium or Slide-seq datasets to map tissue architecture and cellular interactions.

Quick Start

Load spatial data, run preprocessing, detect SVG and domains, perform deconvolution, and visualize spatial patterns in Squidpy.

Frequently Asked Questions about scientific-spatial-transcriptomics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform end-to-end spatial transcriptomics analysis using Squidpy and Scanpy?▼

End-to-end spatial transcriptomics analysis with Squidpy and Scanpy involves preprocessing Visium or Slide-seq data, detecting spatially variable genes, identifying tissue domains, performing cell-type deconvolution, and examining spatial ligand-receptor interactions.

Can I use cell2location for cell-type deconvolution on spatial transcriptomics data?▼

Yes, cell2location performs cell-type deconvolution in spatial spots by integrating spatial transcriptomics data with reference scRNA-seq datasets to estimate the abundance of distinct cell types across tissue locations.

What is the best way to identify spatial domains and spatially variable genes in tissue data?▼

Identifying spatial domains and spatially variable genes is best achieved using SpatialDE, BayesSpace, or STAGATE within a Squidpy workflow, which detect gene expression patterns and map tissue architecture automatically.

Does this spatial transcriptomics workflow support Visium, MERFISH, and Slide-seq data formats?▼

Yes, this spatial transcriptomics workflow supports Visium, MERFISH, and Slide-seq data formats, allowing you to preprocess spatial data and perform quality control and normalization across these common spatial library platforms.

How do I analyze spatial ligand-receptor interactions to study inter-domain signaling?▼

Analyzing spatial ligand-receptor interactions is done by computing proximity metrics within a Squidpy workflow to examine inter-domain signaling and identify cell-cell communication patterns across identified tissue domains.

What are the limitations of using STAGATE and BayesSpace for spatial domain discovery?▼

STAGATE and BayesSpace are specialized for spatial domain discovery, but their limitations include requiring careful parameter tuning for different tissue types and potential computational intensity when processing high-resolution spatial transcriptomics datasets.