stereo-seq

What problem does it solve?

Stereo-seq data analysis requires integrating high-resolution spatial coordinates with gene expression to map tissue architecture and spatial gene programs. This skill provides end-to-end guidance for processing, normalizing, and interpreting Stereo-seq datasets across samples.

Core Features & Use Cases

• High-resolution spatial analysis: process coordinates, binning, and spatially-aware normalization to reveal tissue structure.
• Multi-omics integration-ready: supports Python (scanpy) and R (Seurat) workflows for integration with expression data.
• Use Case: identify spatially variable genes and subcellular patterns to understand tissue microenvironments.

Quick Start

Load the Stereo-seq data, normalize it, and perform initial visualization of spatial gene patterns using Python (scanpy) or R (Seurat).