# Cell Type Annotation

ALHENA distills spatial transcriptomic information into a routine H&E encoder that reads only images at inference. Rather than regressing individual genes, it trains the H&E representation to match the sample-to-sample similarity structure of two frozen transcriptomic models at cell and neighborhood scales. Across 110 cancer-type and signature combinations in TCGA, mean Spearman correlation was 0.341 versus 0.161 for unadapted UNI2-h; on the Thunder pathology benchmark the median change was −0.004.

ALHENA distills spatial transcriptomic information into a routine H&E encoder that reads only images at inference. Rather than regressing individual genes, it trains the H&E representation to match the sample-to-sample similarity structure of two frozen transcriptomic models at cell and neighborhood scales. Across 110 cancer-type and signature combinations in TCGA, mean Spearman correlation was 0.341 versus 0.161 for unadapted UNI2-h; on the Thunder pathology benchmark the median change was −0.004.

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.

Regulatory network inference no longer needs cell types called first. Earlier pipelines clustered cells, annotated types with marker genes, then inferred one network per cluster, so clustering and annotation errors propagated into the network. NetMap infers a gene regulatory network per cell before any clustering, uses GradientShap to attribute network edges to input genes, and reaches clustering scores of 1.0, 0.99 and 0.99 across three PBMC multiome evaluation cases.

Regulatory network inference no longer needs cell types called first. Earlier pipelines clustered cells, annotated types with marker genes, then inferred one network per cluster, so clustering and annotation errors propagated into the network. NetMap infers a gene regulatory network per cell before any clustering, uses GradientShap to attribute network edges to input genes, and reaches clustering scores of 1.0, 0.99 and 0.99 across three PBMC multiome evaluation cases.