How does spatial transcriptomics data analysis differ from standard scRNA-Seq?

Spatial transcriptomics captures gene expression with spatial context, requiring specialized analysis: 1) Technology-specific preprocessing - Visium (10x) uses spot-based deconvolution; MERFISH/seqFISH provides single-cell resolution but limited gene panels; Slide-seq uses beads. 2) Quality control - spatial artifacts, tissue edge effects, spot cell composition. 3) Normalization - must account for spatial variation in cell density and RNA capture efficiency. 4) Spatial analysis - identify spatially variable genes (SpatialDE, SPARK), spatially co-expressed gene modules, domain detection through spatial clustering (BayesSpace, SpaGCN uses graph neural networks). 5) Cell type deconvolution - estimate cell type proportions per spot using scRNA-Seq references (SPOTlight, Cell2location, RCTD). 6) Integration - combine with histology images, multi-modal data. 7) Spatial statistics - Moran's I, Ripley's K for point patterns.