Individual skill
deepspot-m
Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.
skills/deepspot-m/SKILL.md
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# DeepSpot-M
## Overview
DeepSpot-M is a multimodal foundation model that maps a 224x224 H&E histology tile to
spatial gene expression in log1p-CPM. The output is virtual spatial transcriptomics: one
value per queried gene per tile, laid out on the grid the tiles came from.
A LoRA-adapted pathology foundation backbone (Midnight) tokenises the tile. A
cross-attention gene decoder lets each gene query attend to the patch tokens, and a gene
router hypernetwork builds gene-specific projections from frozen biological embeddings
(Evo 2, Orthrus, ProtT5, scGPT, Apertus). Genes enter the model as queryable embeddings
rather than fixed output slots, so the released model covers a ~19k protein-coding gene
panel including genes unseen in training. The panel ships with the weights as