| --- |
| language: |
| - en |
| pretty_name: "HEST-1k Visium Virtual Single-Cell Spatial Transcriptomics (DeepSpot2Cell)" |
| tags: |
| - spatial-transcriptomics |
| - histology |
| - pathology |
| - single-cell |
| - virtual-single-cell |
| - transcriptomics |
| - machine-learning |
| - HEST |
| - DeepSpot2Cell |
| size_categories: |
| - 1M<n<10M |
| license: cc-by-nc-sa-4.0 |
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| --- |
| |
| # HEST-1k Visium virtual single-cell spatial transcriptomics |
|
|
| Predicted **virtual single-cell** gene expression for HEST-1k Visium samples, |
| produced with **DeepSpot2Cell**. DeepSpot2Cell is a Deep Sets model that predicts |
| transcriptomic profiles at single-cell resolution from H&E images and CellViT cell |
| segmentations, trained with spot-level Visium supervision only (no single-cell |
| ground truth). At inference every detected cell in each Visium spot receives its |
| own predicted expression vector. |
|
|
| **Authors**: Max Neuwinger, Kalin Nonchev, Glib Manaiev, Viktor Hendrik Koelzer, |
| and Gunnar Raetsch. |
|
|
| **Code**: https://github.com/ratschlab/DeepSpot2Cell - the DeepSpot2Cell preprint |
| is on bioRxiv (see the citation below). |
|
|
| ## Layout |
|
|
| Files are organized per organ; the virtual single-cell predictions are gzip |
| AnnData (`.h5ad`) keyed by the HEST-1k sample id: |
|
|
| ``` |
| <organ>/virtual_sc/<sample_id>.h5ad # e.g. bowel/virtual_sc/TENX147.h5ad |
| <organ>/thumbnails/<sample_id>_downscaled_fullres.jpeg |
| <organ>/tissue_seg/<sample_id>_contours.geojson (+ _vis.jpg) |
| <organ>/spatial_plots/<sample_id>_spatial_plots.png |
| <organ>/metadata/<sample_id>.json |
| <organ>/{gene_list.json,gene_stats.csv,sample_manifest.csv} |
| ``` |
|
|
| Per-sample metadata is in `metadata.csv` (join key `id`); it merges the HEST-1k |
| clinical/technical fields with this dataset's `split`, `n_cells`, `um_per_px` and |
| `patch_size_px`. Raw H&E whole-slide images are **not** re-hosted here (they are |
| identical to HEST-1k - see Download). |
|
|
| ## Contents (1 samples across 1 organs, 6,030 virtual cells) |
|
|
| | Organ | #samples | #virtual cells | |
| |---|---|---| |
| | Breast | 1 | 6,030 | |
|
|
| *Auto-generated by the upload pipeline on 2026-07-17. Upload in progress - counts grow as files land.* |
|
|
| ## Download |
|
|
| This is a **gated** dataset, so log in first (`pip install huggingface_hub`): |
|
|
| ```python |
| from huggingface_hub import login, snapshot_download, hf_hub_download |
| login(token="YOUR_HF_TOKEN") |
| |
| repo = "ratschlab/HEST_Visium_virtual_single_cell_transcriptomics" |
| local_dir = "DS2C_data" |
| |
| # one organ - virtual single-cell predictions only |
| snapshot_download(repo, repo_type="dataset", local_dir=local_dir, |
| allow_patterns="bowel/virtual_sc/*") |
| |
| # one organ - everything (predictions + thumbnails + segmentations + metadata) |
| snapshot_download(repo, repo_type="dataset", local_dir=local_dir, |
| allow_patterns="lung/*") |
| |
| # all virtual single-cell predictions across every organ |
| snapshot_download(repo, repo_type="dataset", local_dir=local_dir, |
| allow_patterns="*/virtual_sc/*") |
| |
| # gene panels + per-organ manifests only |
| snapshot_download(repo, repo_type="dataset", local_dir=local_dir, |
| allow_patterns=["*/gene_list.json", "*/gene_stats.csv", |
| "*/sample_manifest.csv"]) |
| |
| # specific samples (ids match HEST-1k) |
| snapshot_download(repo, repo_type="dataset", local_dir=local_dir, |
| allow_patterns=["bowel/virtual_sc/TENX147.h5ad", |
| "lung/virtual_sc/TENX62.h5ad"]) |
| ``` |
|
|
| Use the top-level `metadata.csv` (join key `id`) to select samples by organ, |
| cancer type, disease state or train/val split before downloading: |
|
|
| ```python |
| import pandas as pd |
| meta = pd.read_csv(hf_hub_download(repo, "metadata.csv", repo_type="dataset")) |
| sel = meta.loc[(meta["organ"] == "Lung") & (meta["split"] == "val")] |
| snapshot_download(repo, repo_type="dataset", local_dir=local_dir, |
| allow_patterns=[f"{o.lower()}/virtual_sc/{i}.h5ad" |
| for o, i in zip(sel["organ"], sel["id"])]) |
| ``` |
|
|
| > **Raw H&E whole-slide images are not hosted here.** They are byte-identical to |
| > [HEST-1k](https://huggingface.co/datasets/MahmoodLab/hest); download the WSIs, |
| > full-resolution patches and CellViT segmentations from there using the same |
| > sample ids. |
|
|
| ## Loading and plotting |
|
|
| Each virtual-SC file is an AnnData (`cells x genes`): |
|
|
| ```python |
| import io, numpy as np, PIL.Image, scanpy as sc, squidpy as sq |
| |
| adata = sc.read_h5ad("bowel/virtual_sc/TENX147.h5ad") |
| adata |
| # AnnData object with n_obs x n_vars = 97821 x 17956 |
| # obs: 'cell_id', 'spot_barcode', 'is_inside_spot' |
| # var: 'gene' |
| # uns: 'sample_id', 'organ', 'oncotree_code', 'disease_state', 'species', |
| # 'um_per_px', 'patch_size_px', 'expression_space', 'normalization', |
| # 'fullres_px_width', 'fullres_px_height', 'thumbnail_jpeg', 'thumbnail_shape' |
| # obsm: 'spatial' # (he_x, he_y) in full-resolution WSI pixels |
| ``` |
|
|
| Overlay predicted expression on the embedded H&E thumbnail: |
|
|
| ```python |
| img = np.asarray(PIL.Image.open(io.BytesIO(adata.uns["thumbnail_jpeg"].tobytes()))) |
| lib = str(adata.uns["sample_id"]) |
| adata.uns["spatial"] = {lib: {"images": {"hires": img / 255.0}, |
| "scalefactors": {"tissue_hires_scalef": 1.0, |
| "spot_diameter_fullres": 1.0}}} |
| |
| # obsm['spatial'] is (he_x, he_y) in full-res pixels; scale to the thumbnail and |
| # swap to (row, col) for squidpy. |
| scale = img.shape[1] / float(adata.uns["fullres_px_width"]) |
| adata.obsm["spatial_plot"] = (adata.obsm["spatial"] * scale)[:, [1, 0]] |
| |
| sq.pl.spatial_scatter(adata, basis="spatial_plot", |
| color=[None, "CEACAM5", "CD3E", "COL1A1"], |
| title=["H&E", "CEACAM5 (tumour)", "CD3E (T cell)", "COL1A1 (stroma)"], |
| size=4, img_alpha=0.9, ncols=2, library_id=lib) |
| ``` |
|
|
| ## Data organization |
|
|
| | Field | Description | |
| |-------|-------------| |
| | `adata.X` | Dense **float16** matrix (cells x genes), log1p-normalized expression, clipped >= 0, **rounded to 3 decimals** (see note). | |
| | `adata.obs['cell_id']` | Unique cell identifier, traceable to the CellViT segmentation. | |
| | `adata.obs['spot_barcode']` | Visium spot the cell belongs to (`patch_<row>_<col>_<sample_id>`). | |
| | `adata.obs['is_inside_spot']` | 1 if the nucleus centroid falls inside the 55 um Visium spot, else 0. | |
| | `adata.var['gene']` | Gene names (organ-specific panel; see `gene_list.json`). | |
| | `adata.obsm['spatial']` | Cell centroid `(he_x, he_y)` in full-resolution WSI pixels. | |
| | `adata.uns['sample_id']` | HEST-1k sample id. | |
| | `adata.uns['um_per_px']` | Microns per pixel of the source WSI. | |
| | `adata.uns['patch_size_px']` | Inference patch crop size (pixels). | |
| | `adata.uns['expression_space']` | `"log1p_norm10k"`. | |
| | `adata.uns['thumbnail_jpeg']` | JPEG-encoded H&E overview (1-D uint8); `thumbnail_shape` is `[H, W, 3]`. | |
| | `adata.uns['organ']`, `['oncotree_code']`, `['disease_state']`, ... | HEST-1k metadata fields. | |
|
|
| **Expression space:** `log1p(10000 * raw_counts / total_counts_in_spot)` - standard |
| scanpy `normalize_total` + `log1p`. Recover approximate normalized counts with `np.expm1()`. |
|
|
| **Rounding note:** to keep the atlas compact, `X` is rounded to **3 decimal places** |
| in log1p space before storage (absolute error below 1e-3, mean 2.4e-4; the values |
| are float16 model predictions, so this is within their intrinsic precision, and it |
| roughly halves the download). |
|
|
| ## Companion assets (per organ) |
|
|
| Alongside `virtual_sc/`, each organ ships: |
|
|
| - `thumbnails/<id>_downscaled_fullres.jpeg` - H&E overview. |
| - `tissue_seg/<id>_contours.geojson` (+ `<id>_vis.jpg`) - CellViT tissue/cell contours. |
| - `spatial_plots/<id>_spatial_plots.png` - spot-overlay QC visualization. |
| - `metadata/<id>.json` - per-sample HEST-1k metadata. |
| - `gene_list.json`, `gene_stats.csv`, `sample_manifest.csv` - panel + sample summary. |
|
|
| ## Gene panels |
|
|
| Panels use a hybrid strategy: Xenium anchor genes present in >=50% of Visium |
| training samples come first (for cross-platform validation), then Visium HVGs |
| ranked by consensus votes across training samples. |
|
|
| | | Bowel | Breast | Lung | |
| |--|-------|--------|------| |
| | Xenium anchor genes | 409 | 393 | 286 | |
| | Visium filler genes | 17,547 | 18,029 | 13,929 | |
| | **Total** | **17,956** | **18,422** | **14,215** | |
|
|
| ## Performance |
|
|
| Accuracy is evaluated on held-out **Xenium** test samples (true single-cell |
| measurements) by Pearson correlation between predicted and measured per-gene |
| expression. |
|
|
|  |
|
|
| ## Model |
|
|
| - **Architecture** - DeepSpot2Cell: a Deep Sets model with multi-head cell |
| attention and neighbourhood context. |
| - **Foundation model** - H-Optimus-1 (patch + cell embeddings). |
| - **Cell segmentation** - CellViT++ (from HEST-1k). |
| - **Supervision** - spot-level Visium only; no single-cell ground truth is seen |
| during training. |
| - **Loss** - MSE + Pearson-correlation loss on log1p-normalized spot expression. |
|
|
| ## How to cite |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @article{nonchev2025deepspot2cell, |
| title = {DeepSpot2Cell: Predicting Virtual Single-Cell Spatial Transcriptomics |
| from H&E Images using Spot-Level Supervision}, |
| author = {Nonchev, Kalin and Manaiev, Glib and Koelzer, Viktor H. and Raetsch, Gunnar}, |
| journal = {bioRxiv}, |
| year = {2025}, |
| doi = {10.1101/2025.09.23.678121} |
| } |
| |
| @article{jaume2024hest, |
| title = {HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis}, |
| author = {Jaume, Guillaume and others}, |
| journal = {Advances in Neural Information Processing Systems}, |
| year = {2024} |
| } |
| ``` |
|
|
| #### NB |
| This dataset is a companion to [HEST-1k](https://huggingface.co/datasets/MahmoodLab/hest); |
| sample ids match HEST-1k exactly. The H&E images and Visium data originate from |
| HEST-1k - please also respect HEST-1k's terms of access. Computational analysis |
| was performed at [Leonhard Med](https://sis.id.ethz.ch/services/sensitiveresearchdata/), |
| the secure trusted research environment at ETH Zurich. |
|
|
