stereo-seq

To analyze Stereo-seq spatial transcriptomics data, you process spatial coordinates with gene expression through binning, normalization, and clustering. This workflow reveals high-resolution tissue architecture and subcellular gene expression patterns across multiple samples.

Yes, this spatial transcriptomics workflow supports both Python and R environments. It provides standard preprocessing and spatial analysis guidance using the Scanpy framework for Python and the Seurat package for R.

The best way to identify spatially variable genes in high-resolution spatial omics datasets is to apply spatially-aware normalization and clustering. This approach maps tissue microenvironments and detects subcellular patterns across samples.

Processing spatial coordinates for Stereo-seq data involves loading the raw dataset, applying coordinate binning, and running spatially-aware normalization. This prepares the high-resolution spatial omics data for downstream clustering and visualization.

Yes, you can use this skill to prepare data for multi-omics integration. It supports standard preprocessing in Python and R workflows, enabling integration of spatial transcriptomics coordinates with gene expression matrices.

Spatial transcriptomics clustering requires spatially-aware normalization because standard methods ignore physical bin coordinates. Applying spatial normalization preserves tissue architecture and accurately maps subcellular microenvironments during clustering.