Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide

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 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. Licensing 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. Installation 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. Model access The weights are gated: 1. Open <https://huggingface.co/ratschlab/DeepSpotM and request access. 2. Once access is granted, authenticate the machine that will download them: from pretrained reads that cached token, so a login is needed once per machine. Quick start 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. Tile requirements 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: 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. Keep the dependency optional 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: Embedding sources 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. Whole slide workflow Prediction is per tile, so a slide scale run is a tiling step followed by batched inference: 1. Extract 224x224 tiles on a grid with the histolab skill, keeping each tile's coordinates. 2. Process and stack tiles into batches with torch.stack . 3. Call predict genes once per batch with the same gene list. 4. Concatenate the batches into a tiles by genes matrix and attach the coordinates. 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. Common use cases Spatial expression maps for marker genes across a tumour section. Transcriptome wide prediction over a slide cohort with no matching assay run. Querying any of the ~19k panel genes by symbol, including genes unseen in training — far beyond the few hundred genes of a typical spatial assay panel. Adding an expression channel to a morphology only histology pipeline. Building a slide level cohort atlas, as done for TCGA. Detailed references 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. Primary sources Paper: <https://doi.org/10.64898/2026.06.19.26356060 (medRxiv, posted 22 June 2026) Code: <https://github.com/ratschlab/DeepSpotM Weights: <https://huggingface.co/ratschlab/DeepSpotM PyPI: <https://pypi.org/project/deepspotm/

deepspot-m