spatial-transcriptomics-spatial-data-io

Loading Visium and Xenium spatial transcriptomics data into a unified format requires parsing platform-specific outputs to assemble standardized AnnData or SpatialData objects. This Skill identifies Space Ranger and Xenium outputs, extracting expression matrices, coordinates, and tissue images into a single structure.

Converting between SpatialData and AnnData objects preserves spatial coordinates in obsm['spatial'] and images in uns['spatial']. This Skill maintains coordinates, tissue images, and platform-specific metadata across formats during the conversion process.

Supported spatial transcriptomics platforms include Visium, Xenium, MERFISH, Slide-seq, CosMx, and Stereo-seq. The Skill identifies these platform-specific outputs and loads expression matrices, coordinates, and images into unified data objects.

Parsing platform outputs into spatial data structures requires squidpy, spatialdata_io, spatialdata, scanpy, and anndata. These dependencies are necessary to assemble robust spatial data structures from diverse spatial transcriptomics platform outputs.

A unified spatial data object for diverse platform outputs enables seamless downstream analyses and immediate visualization. Standardizing outputs from platforms like Xenium and MERFISH into AnnData preserves tissue images and spatial coordinates in accessible fields.

Loading Slide-seq and CosMx data preserves platform-specific metadata within the assembled SpatialData or AnnData representations. The Skill exposes spatial coordinates and tissue images while maintaining the original platform metadata across formats.