spatial-transcriptomics-tutorials-with-omicverse

You can perform spatial transcriptomics deconvolution on Visium data through OmicVerse tutorials that validate spatial coordinates and apply Tangram or cell2location to estimate cell type abundances across tissue spots.

Preprocessing Stereo-seq spatial transcriptomics data is handled by OmicVerse workflows that crop, rotate, and align spatial coordinates before validating numeric types for downstream clustering and trajectory analyses.

OmicVerse spatial transcriptomics workflows support Slide-seq datasets across preprocessing, deconvolution, and downstream analyses, requiring validation that spatial coordinates exist and are numeric before executing analytical steps.

Spatial transcriptomics integration across multiple tissue sections uses STAligner, GraphST, and SpaceFlow within OmicVerse to align spatial coordinates and resolve batch effects for unified downstream clustering.

Validating spatial coordinates before running spatial transcriptomics clustering is required to ensure coordinates exist and are numeric, preventing execution errors during downstream STAGATE clustering and trajectory analyses.

Downstream analyses available for spatial transcriptomics data after deconvolution include clustering, integration, trajectory inference, and cell communication, applied to Visium, Stereo-seq, and Slide-seq datasets within OmicVerse.