# SSAF-T

Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.

Spatial transcriptomics analysis has long depended on cell segmentation, whose boundaries and transcript assignments are error-prone; segmentation-free methods avoid that step but often need an external single-cell reference or a preset number of cell types. SSAF-T chains segmentation-free cell typing to segmentation: the agent CeLLMan labels clusters from a literature-grounded knowledge graph and marker genes, the labels are spread across the tissue map, and that map constrains nucleus-seeded watershed segmentation. On public data including Visium HD mouse small intestine, Stereo-seq mouse brain, Xenium mouse kidney and MERFISH mouse liver, cell-type distributions match tissue anatomy, segmentation is comparable to Baysor and Cellpose, and runs are faster.