giotto

Spatial transcriptomics analysis in R is performed by creating Giotto objects to process raw counts from platforms like Visium, Slide-seq, MERFISH, seqFISH, and Xenium. This enables integrated spatial context, spatial networks, and statistics.

Yes, spatial genomics workflows support integrating single-cell references for deconvolution. You can manage and integrate Giotto objects with single-cell data to perform comparative analyses and identify cell-type localization within tissue architecture.

The best way to visualize spatial patterns is by generating rich, publication-ready visualizations from spatial networks and statistics. This reveals spatial patterns and spatially informed differential expression across multi-platform datasets.

Yes, you can use Giotto for end-to-end workflows from raw counts to publication-ready figures. It provides reproducible workflows and best-practice guidance for complete spatial transcriptomics studies, satisfying spatial analysis and visualization requirements.

Spatial statistics are needed when analyzing tissue architecture to reveal spatial patterns and perform spatially informed differential expression. These statistics integrate spatial context to identify cell-type localization within tissue datasets.

Spatial analysis tools in R like Giotto work with seqFISH and Xenium data by parsing raw counts into Giotto objects. This enables spatial data processing, spatial networks, and high-quality visualizations for these specific platforms.