Spatialsnake: a flexible workflow for spatial transcriptomics analysis across platforms

Abstract

Background: Spatial transcriptomics technologies measure gene expression at different spatial resolutions and generate diverse data outputs. Existing workflows support a range of platforms and analyses, but combining methods and extending analyses to selected populations or tissue regions can require additional scripts and repeated data preparation. Findings: We developed Spatialsnake, a flexible workflow built on SpatialData and Snakemake for analysing processed outputs from Visium, Visium HD, Stereo-seq, Xenium and MERFISH. A common command-line interface supports single- and multi-sample analysis, with adjustable parameters and saved intermediate results. Users can select populations, tissue regions or distance-based neighborhoods, repeat clustering and annotation, and map refined labels back to the full dataset. Additional modules support cell-type composition estimation, spatial-domain identification, cell-cell communication inference, regulon analysis and sample-level comparisons. We demonstrate these functions using mouse-brain Visium and colorectal cancer Visium HD datasets. Joint analysis of three colorectal cancer sections retained 1,527,987 bins and enabled further analysis of 223,632 epithelial bins, linking epithelial subpopulation annotations to their locations within the complete tissue. Conclusions: Spatialsnake provides a unified workflow for spatial transcriptomics analysis across sequencing- and imaging-based platforms. Its flexible analysis modules support both whole-tissue analysis and further investigation of selected cell populations and tissue regions.