skills/deepspot-m/SKILL.md
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
tokens.csv and is exposed as model.gene_names; genes outside it cannot be queried in
this release.
Applied to TCGA, the model produced a virtual spatial transcriptomics atlas of 28,664 slides across 32 cancer types.
The code is PolyForm Noncommercial 1.0.0 and the weights are CC-BY-NC-SA-4.0. Use it for noncommercial research and check both licences before redistributing outputs.
uv pip install deepspotm==1.0.0
Version 1.0.0 targets Python 3.10 to 3.13 and pulls in PyTorch. Install the PyTorch build that matches your CUDA version first if you want GPU inference.
The weights are gated:
huggingface-cli login
from_pretrained reads that cached token, so a login is needed once per machine.
from deepspotm import DeepSpotM
model, image_processor = DeepSpotM.from_pretrained("ratschlab/DeepSpotM", source="scgpt")
vals = model.predict_genes(image_processor(pil_tile).unsqueeze(0), ["EPCAM", "CD3D"])
pil_tile is a PIL image of exactly 224x224 pixels. image_processor turns it into a
tensor, unsqueeze(0) adds the batch dimension, and predict_genes takes the batch plus a
list of HGNC gene symbols. Values come back in log1p-CPM, aligned with the gene list you
passed, so keep that list beside the output to keep the columns labelled. Symbols must be
in the released ~19k-gene panel (model.gene_names); an unknown symbol raises KeyError
naming the offending genes.
Tiles must be 224x224 RGB at roughly 20x magnification (about 0.5 microns per pixel). Check the size at the boundary of your pipeline rather than passing an unchecked crop through:
TILE_PX = 224
def require_tile(tile):
"""Return an RGB 224x224 tile, or raise if the crop is the wrong size."""
if tile.size != (TILE_PX, TILE_PX):
raise ValueError(
f"DeepSpot-M expects a {TILE_PX}x{TILE_PX} tile at about 20x "
f"(~0.5 microns per pixel); got {tile.size[0]}x{tile.size[1]}. "
"Re-tile at the matching level or resample the crop."
)
return tile.convert("RGB")
Extract tiles at the slide level whose resolution is nearest 0.5 microns per pixel, then crop to 224x224 there. Resampling from a coarser level changes the texture the backbone reads.
deepspotm and its weights are a heavy, gated dependency. Import it inside the function
that needs it so the surrounding project installs, imports and tests without it, and turn
an ImportError into a message that names every step:
DEEPSPOTM_HELP = (
"DeepSpot-M is unavailable. Install it with `uv pip install deepspotm==1.0.0`, request "
"access to the gated weights at https://huggingface.co/ratschlab/DeepSpotM, then "
"authenticate with `huggingface-cli login`."
)
def load_deepspotm(source="scgpt"):
try:
from deepspotm import DeepSpotM
except ImportError as exc:
raise RuntimeError(DEEPSPOTM_HELP) from exc
return DeepSpotM.from_pretrained("ratschlab/DeepSpotM", source=source)
source selects which frozen gene embedding the router builds projections from. It is one
of five values:
source | Gene embedding |
|---|---|
evo2 | genomic sequence |
orthrus | RNA |
prott5 | protein sequence |
scgpt | single-cell expression |
apertus | language model |
Each gives a different view of gene identity. Pick one per run, and run the same tiles
through more than one source when the choice matters to your analysis. See
references/api.md for the full call surface, batching and device placement, gene symbol
handling and output units.
Prediction is per tile, so a slide-scale run is a tiling step followed by batched inference:
histolab skill, keeping each tile's
coordinates.torch.stack.predict_genes once per batch with the same gene list.That matrix is the virtual spatial transcriptomics map for the slide, and it drops
straight into AnnData for downstream spatial analysis. references/whole_slide.md has a
worked loop, batch sizing and an AnnData assembly step.
references/api.md: from_pretrained and predict_genes in full, the five embedding
sources and how to choose, batching, device placement, gene symbol handling, and
converting log1p-CPM output.references/whole_slide.md: tiling with histolab, a slide-scale prediction loop,
assembling and storing a tiles-by-genes matrix, and cohort-scale runs.