Datasets:

ratschlab
/

KPMP_virtual_spatial_transcriptomics_atlas

---
pretty_name: KPMP Virtual Spatial Transcriptomics
language:
- en
tags:
- spatial-transcriptomics
- histology
- pathology
- transcriptomics
- machine-learning
- KPMP
- kidney
- nephrology
- kidney-disease
- gene-expression
- computational-pathology
- whole-slide-imaging
- virtual-spatial-transcriptomics
- foundation-model
- deep-learning
size_categories:
- 1K<n<10K
license: cc-by-nc-sa-4.0
extra_gated_heading: Eligibility declarations required to request access
extra_gated_prompt: >-
  This dataset is released under CC-BY-NC-SA-4.0 strictly for non-commercial
  academic research. Commercial use is not permitted unless explicit written
  permission is granted by the authors.
  ELIGIBILITY. Access is granted only to individuals whose affiliations are
  exclusively academic or public non-profit research institutions. Anyone
  holding any concurrent commercial affiliation — employment, consulting,
  advisory roles, internships, or founding roles at a company or startup — is
  not eligible, regardless of intended use.

  SCOPE. Research performed at, for, funded by, or in collaboration with a
  commercial entity is commercial use and is prohibited. This includes
  corporate R&D, internal evaluation, benchmarking, and proof-of-concept
  work, even if exploratory and never productized. "Research" conducted in a
  commercial setting does not qualify as non-commercial academic research.

  REVIEW. All requests are reviewed manually against these criteria; please
  allow 3-5 business days. By submitting, you agree to share your contact
  information (email and username) with the repository authors. Vague,
  incomplete, or inaccurate submissions will be declined. False or incomplete
  declarations void any access granted and constitute a violation of the
  license terms. For commercial licensing, contact the maintainers.
extra_gated_fields:
  Full name (first and last): text
  Academic affiliation (full official name, no abbreviations): text
  Position at this institution:
    type: text
    help: E.g. PhD student, postdoc, research scientist, professor.
  Official institutional email:
    type: text
    help: >-
      Must match the primary email on your Hugging Face account and belong to
      a recognized academic or public research institution's domain. Personal
      domains (gmail, hotmail, qq, etc.) and corporate domains will be denied.
  Concurrent commercial affiliations (write "None" if none):
    type: text
    help: >-
      Disclose ALL current commercial ties: employment, consulting, advisory
      positions, internships, and founded or co-founded startups. Individuals
      with any concurrent commercial affiliation are not eligible. Failure to
      disclose is grounds for denial and revocation of access.
  Intended use (be specific):
    type: text
    help: >-
      Name your institution/lab, the broader research project or area, and
      concretely how the dataset will be used (e.g. model training,
      benchmarking, validation). Generic answers such as "for research
      purposes" will be declined.
  I declare that I hold no concurrent affiliation with any company, startup, or other commercial entity, and that I am requesting access solely in my academic capacity: checkbox
  I declare that this dataset and any derivatives will be used exclusively for non-commercial academic research, and will not be used to train, evaluate, or develop any model, product, or service intended for commercial use or release under a different license: checkbox
  I declare that I will not share the dataset or any part of it with any commercial entity or any individual who has not been granted access, and will not re-upload or mirror it in any location that bypasses this access process: checkbox
  I declare that if my status changes and I take on any commercial affiliation, or if my access is revoked, I will cease all use and delete all copies of the dataset: checkbox
  I declare that all information in this request is truthful and complete, and I understand that any misrepresentation voids my access and violates the license terms: checkbox
  I confirm that I have read the license terms and agree to retain attribution, license any derivatives under the same license, and cite the associated publication in any work using this dataset:
    type: checkbox
    help: >-
      The license is CC-BY-NC-SA-4.0. Redistribution and derivatives are
      permitted only under this same license, with attribution and
      non-commercial use.
  I agree to receive news and updates about this technology: checkbox
extra_gated_button_content: Submit declarations and request access
---
# KPMP virtual spatial transcriptomics

Virtual spatial transcriptomics for **1,534 H&E whole-slide images** (fresh-frozen and FFPE) of kidney biopsies from
**476 participants** of the [Kidney Precision Medicine Project (KPMP)](https://www.kpmp.org): **19,338 genes** in
each of **5,458,825 spots** on a 56 µm grid, predicted with **DeepSpot-M Kidney**.

**Model**: DeepSpot-M Kidney, finetuned from [ratschlab/DeepSpotM](https://huggingface.co/ratschlab/DeepSpotM) ·
**Code**: [github.com/ratschlab/DeepSpotM](https://github.com/ratschlab/DeepSpotM) · **Paper**: citation will be added
on publication; DeepSpot-M: [medRxiv 2026.06.19.26356060](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060).

## News

  - [09.2026] Introducing [Aurora](https://www.auroraomics.org) - a no-code platform for virtual spatial transcriptomics from H&E images. Try the interactive [Aurora demo](https://www.auroraomics.org/demo).

## Contents

| Enrollment group | FF slides | FFPE slides | Participants | Spots |
|---|---:|---:|---:|---:|
| AKI | 28 | 68 | 33 | 251,920 |
| CKD | 485 | 819 | 401 | 4,779,144 |
| DM-R | 53 | 72 | 36 | 387,565 |
| Healthy Reference | – | 9 | 6 | 40,196 |
| **Total** | **566** | **968** | **476** | **5,458,825** |

```
data/<ENROLLMENT_GROUP>/<FF|FFPE>/<slide_id>.h5ad    one AnnData file per slide (144 GB in total)
metadata.csv                                         one row per slide
```

## Example

![KPMP AKI biopsy: H&E and virtual LRP2 / UMOD / COL1A1](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_aki.png)

FFPE biopsy of the AKI group: H&E and virtual **LRP2** (proximal tubule), **UMOD** (thick ascending limb) and
**COL1A1** (fibrosis and stroma).

<details>
<summary>More examples (CKD FF, DM-R FFPE, Healthy Reference FFPE)</summary>

![KPMP CKD biopsy (FF)](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_ckd.png)
![KPMP DM-R biopsy (FFPE)](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_dmr.png)
![KPMP Healthy Reference biopsy (FFPE)](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_healthy_reference.png)
</details>

## Download

This is a gated dataset: log in first (`pip install huggingface_hub`).

```python
import pandas as pd
from huggingface_hub import login, snapshot_download, hf_hub_download
login(token="YOUR_HF_TOKEN")
repo = "ratschlab/KPMP_virtual_spatial_transcriptomics_atlas"
# one enrollment group and slide type, e.g. AKI FFPE ("data/*/FF/*" for all fresh-frozen slides)
snapshot_download(repo, repo_type="dataset", local_dir="KPMP_data", allow_patterns="data/AKI/FFPE/*")
# or select slides in metadata.csv first
meta = pd.read_csv(hf_hub_download(repo, "metadata.csv", repo_type="dataset"), low_memory=False)
sel = meta[meta["Primary Adjudicated Category"] == "Diabetic Kidney Disease"]
snapshot_download(repo, repo_type="dataset", local_dir="KPMP_data", allow_patterns=sel.file_path.tolist())
```

## Usage

```python
import numpy as np
import scanpy as sc
import squidpy as sq
adata = sc.read_h5ad("KPMP_data/data/AKI/FFPE/ba1203a0-db6f-43ec-a80a-ebb85c2ee193_S-2406-007749_HE_4of4.h5ad")
adata.X = adata.X.astype(np.float32)
sq.pl.spatial_scatter(adata, color=[None, "LRP2", "UMOD", "COL1A1"], shape="square", ncols=2)
```

## Data format

- **`X`**: virtual expression of 19,338 genes per spot, log1p counts per 10,000 (log1p CP10k), stored as float16
  (cast to float32 before analysis).
- **`obs`**: one row per spot: `participant_id`, `enrollment`, `slide_type`, `section` (tissue piece on the slide,
  1 = largest), lattice position (`array_row`, `array_col`), centre in full-resolution pixels (`x_pixel`, `y_pixel`)
  and per-spot image QC (`qc_*`).
- **`var`**: HGNC gene symbol, `ensembl_ids`, `hgnc_id`.
- **`obsm['spatial']`** and **`uns['spatial']`**: spot centres and an H&E overview image, as scanpy and squidpy expect.
- **`uns['he2st']`**: slide and processing metadata.
- **`metadata.csv`**: one row per slide: file path, participant, enrollment group, slide type, number of spots, the
  KPMP open-access clinical table, the KPMP pathology descriptor scores (`TIV: …` columns) and the KPMP download
  link of the whole-slide image (`svs_url`).

Each spot is a 56 µm square of a non-overlapping lattice over the tissue; its expression is predicted from the 65 µm
H&E field of view centred on it. One model trained on KPMP 10x Visium biopsies of other participants was applied to
every slide.

## How to cite

Please cite DeepSpot-M (the citation of the kidney paper will be added on publication):

```bibtex
@article{nonchev2026deepspotm,
  title={DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology},
  author={Nonchev, Kalin and Dawo, Sebastian and Silina, Karina and Koelzer, Viktor Hendrik and Raetsch, Gunnar},
  journal={medRxiv},
  pages={2026--06},
  year={2026},
  publisher={Cold Spring Harbor Laboratory Press}
}
```

## KPMP acknowledgement

The H&E slides, clinical data and pathology descriptor scores are KPMP open-access data. Work that uses this dataset
must acknowledge KPMP ([Citing KPMP data](https://www.kpmp.org/help-docs/citing-kpmp-data)):

> The results here are in whole or part based upon data generated by the Kidney Precision Medicine Project. Accessed Month Day, Year. https://www.kpmp.org.
>
> The Kidney Precision Medicine Project (KPMP) is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) through the following grants: U01DK133081, U01DK133091, U01DK133092, U01DK133093, U01DK133095, U01DK133097, U01DK114866, U01DK114908, U01DK133090, U01DK133113, U01DK133766, U01DK133768, U01DK114907, U01DK114920, U01DK114923, U01DK114933, U24DK114886, UH3DK114926, UH3DK114861, UH3DK114915, UH3DK114937, 1U01DK144965-01, 1U01DK144994-01. We gratefully acknowledge the essential contributions of our patient participants and the support of the American public through their tax dollars.

## License

[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/), non-commercial academic research only. The
KPMP data included remain subject to KPMP's terms of use.

#### NB
Computational data analysis was performed at [Leonhard Med](https://sis.id.ethz.ch/services/sensitiveresearchdata/), the secure trusted research environment at ETH Zurich. Our pipeline aligns with the specific cluster requirements and resources.

	---
	pretty_name: KPMP Virtual Spatial Transcriptomics
	language:
	- en
	tags:
	- spatial-transcriptomics
	- histology
	- pathology
	- transcriptomics
	- machine-learning
	- KPMP
	- kidney
	- nephrology
	- kidney-disease
	- gene-expression
	- computational-pathology
	- whole-slide-imaging
	- virtual-spatial-transcriptomics
	- foundation-model
	- deep-learning
	size_categories:
	- 1K<n<10K
	license: cc-by-nc-sa-4.0
	extra_gated_heading: Eligibility declarations required to request access
	extra_gated_prompt: >-
	This dataset is released under CC-BY-NC-SA-4.0 strictly for non-commercial
	academic research. Commercial use is not permitted unless explicit written
	permission is granted by the authors.

	ELIGIBILITY. Access is granted only to individuals whose affiliations are
	exclusively academic or public non-profit research institutions. Anyone
	holding any concurrent commercial affiliation — employment, consulting,
	advisory roles, internships, or founding roles at a company or startup — is
	not eligible, regardless of intended use.

	SCOPE. Research performed at, for, funded by, or in collaboration with a
	commercial entity is commercial use and is prohibited. This includes
	corporate R&D, internal evaluation, benchmarking, and proof-of-concept
	work, even if exploratory and never productized. "Research" conducted in a
	commercial setting does not qualify as non-commercial academic research.

	REVIEW. All requests are reviewed manually against these criteria; please
	allow 3-5 business days. By submitting, you agree to share your contact
	information (email and username) with the repository authors. Vague,
	incomplete, or inaccurate submissions will be declined. False or incomplete
	declarations void any access granted and constitute a violation of the
	license terms. For commercial licensing, contact the maintainers.
	extra_gated_fields:
	Full name (first and last): text
	Academic affiliation (full official name, no abbreviations): text
	Position at this institution:
	type: text
	help: E.g. PhD student, postdoc, research scientist, professor.
	Official institutional email:
	type: text
	help: >-
	Must match the primary email on your Hugging Face account and belong to
	a recognized academic or public research institution's domain. Personal
	domains (gmail, hotmail, qq, etc.) and corporate domains will be denied.
	Concurrent commercial affiliations (write "None" if none):
	type: text
	help: >-
	Disclose ALL current commercial ties: employment, consulting, advisory
	positions, internships, and founded or co-founded startups. Individuals
	with any concurrent commercial affiliation are not eligible. Failure to
	disclose is grounds for denial and revocation of access.
	Intended use (be specific):
	type: text
	help: >-
	Name your institution/lab, the broader research project or area, and
	concretely how the dataset will be used (e.g. model training,
	benchmarking, validation). Generic answers such as "for research
	purposes" will be declined.
	I declare that I hold no concurrent affiliation with any company, startup, or other commercial entity, and that I am requesting access solely in my academic capacity: checkbox
	I declare that this dataset and any derivatives will be used exclusively for non-commercial academic research, and will not be used to train, evaluate, or develop any model, product, or service intended for commercial use or release under a different license: checkbox
	I declare that I will not share the dataset or any part of it with any commercial entity or any individual who has not been granted access, and will not re-upload or mirror it in any location that bypasses this access process: checkbox
	I declare that if my status changes and I take on any commercial affiliation, or if my access is revoked, I will cease all use and delete all copies of the dataset: checkbox
	I declare that all information in this request is truthful and complete, and I understand that any misrepresentation voids my access and violates the license terms: checkbox
	I confirm that I have read the license terms and agree to retain attribution, license any derivatives under the same license, and cite the associated publication in any work using this dataset:
	type: checkbox
	help: >-
	The license is CC-BY-NC-SA-4.0. Redistribution and derivatives are
	permitted only under this same license, with attribution and
	non-commercial use.
	I agree to receive news and updates about this technology: checkbox
	extra_gated_button_content: Submit declarations and request access
	---

	# KPMP virtual spatial transcriptomics

	Virtual spatial transcriptomics for 1,534 H&E whole-slide images (fresh-frozen and FFPE) of kidney biopsies from
	476 participants of the [Kidney Precision Medicine Project (KPMP)](https://www.kpmp.org): 19,338 genes in
	each of 5,458,825 spots on a 56 µm grid, predicted with DeepSpot-M Kidney.

	Model: DeepSpot-M Kidney, finetuned from [ratschlab/DeepSpotM](https://huggingface.co/ratschlab/DeepSpotM) ·
	Code: [github.com/ratschlab/DeepSpotM](https://github.com/ratschlab/DeepSpotM) · Paper: citation will be added
	on publication; DeepSpot-M: [medRxiv 2026.06.19.26356060](https://www.medrxiv.org/content/10.64898/2026.06.19.26356060).

	## News

	- [09.2026] Introducing [Aurora](https://www.auroraomics.org) - a no-code platform for virtual spatial transcriptomics from H&E images. Try the interactive [Aurora demo](https://www.auroraomics.org/demo).

	## Contents

	\| Enrollment group \| FF slides \| FFPE slides \| Participants \| Spots \|
	\|---\|---:\|---:\|---:\|---:\|
	\| AKI \| 28 \| 68 \| 33 \| 251,920 \|
	\| CKD \| 485 \| 819 \| 401 \| 4,779,144 \|
	\| DM-R \| 53 \| 72 \| 36 \| 387,565 \|
	\| Healthy Reference \| – \| 9 \| 6 \| 40,196 \|
	\| Total \| 566 \| 968 \| 476 \| 5,458,825 \|

	```
	data/<ENROLLMENT_GROUP>/<FF\|FFPE>/<slide_id>.h5ad one AnnData file per slide (144 GB in total)
	metadata.csv one row per slide
	```

	## Example

	![KPMP AKI biopsy: H&E and virtual LRP2 / UMOD / COL1A1](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_aki.png)

	FFPE biopsy of the AKI group: H&E and virtual LRP2 (proximal tubule), UMOD (thick ascending limb) and
	COL1A1 (fibrosis and stroma).

	<details>
	<summary>More examples (CKD FF, DM-R FFPE, Healthy Reference FFPE)</summary>

	![KPMP CKD biopsy (FF)](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_ckd.png)
	![KPMP DM-R biopsy (FFPE)](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_dmr.png)
	![KPMP Healthy Reference biopsy (FFPE)](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_example_healthy_reference.png)
	</details>

	## Download

	This is a gated dataset: log in first (`pip install huggingface_hub`).

	```python
	import pandas as pd
	from huggingface_hub import login, snapshot_download, hf_hub_download
	login(token="YOUR_HF_TOKEN")

	repo = "ratschlab/KPMP_virtual_spatial_transcriptomics_atlas"

	# one enrollment group and slide type, e.g. AKI FFPE ("data//FF/" for all fresh-frozen slides)
	snapshot_download(repo, repo_type="dataset", local_dir="KPMP_data", allow_patterns="data/AKI/FFPE/*")

	# or select slides in metadata.csv first
	meta = pd.read_csv(hf_hub_download(repo, "metadata.csv", repo_type="dataset"), low_memory=False)
	sel = meta[meta["Primary Adjudicated Category"] == "Diabetic Kidney Disease"]
	snapshot_download(repo, repo_type="dataset", local_dir="KPMP_data", allow_patterns=sel.file_path.tolist())
	```

	## Usage

	```python
	import numpy as np
	import scanpy as sc
	import squidpy as sq

	adata = sc.read_h5ad("KPMP_data/data/AKI/FFPE/ba1203a0-db6f-43ec-a80a-ebb85c2ee193_S-2406-007749_HE_4of4.h5ad")
	adata.X = adata.X.astype(np.float32)
	sq.pl.spatial_scatter(adata, color=[None, "LRP2", "UMOD", "COL1A1"], shape="square", ncols=2)
	```

	## Data format

	- `X`: virtual expression of 19,338 genes per spot, log1p counts per 10,000 (log1p CP10k), stored as float16
	(cast to float32 before analysis).
	- `obs`: one row per spot: `participant_id`, `enrollment`, `slide_type`, `section` (tissue piece on the slide,
	1 = largest), lattice position (`array_row`, `array_col`), centre in full-resolution pixels (`x_pixel`, `y_pixel`)
	and per-spot image QC (`qc_*`).
	- `var`: HGNC gene symbol, `ensembl_ids`, `hgnc_id`.
	- `obsm['spatial']` and `uns['spatial']`: spot centres and an H&E overview image, as scanpy and squidpy expect.
	- `uns['he2st']`: slide and processing metadata.
	- `metadata.csv`: one row per slide: file path, participant, enrollment group, slide type, number of spots, the
	KPMP open-access clinical table, the KPMP pathology descriptor scores (`TIV: …` columns) and the KPMP download
	link of the whole-slide image (`svs_url`).

	Each spot is a 56 µm square of a non-overlapping lattice over the tissue; its expression is predicted from the 65 µm
	H&E field of view centred on it. One model trained on KPMP 10x Visium biopsies of other participants was applied to
	every slide.

	## How to cite

	Please cite DeepSpot-M (the citation of the kidney paper will be added on publication):

	```bibtex
	@article{nonchev2026deepspotm,
	title={DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histology},
	author={Nonchev, Kalin and Dawo, Sebastian and Silina, Karina and Koelzer, Viktor Hendrik and Raetsch, Gunnar},
	journal={medRxiv},
	pages={2026--06},
	year={2026},
	publisher={Cold Spring Harbor Laboratory Press}
	}
	```

	## KPMP acknowledgement

	The H&E slides, clinical data and pathology descriptor scores are KPMP open-access data. Work that uses this dataset
	must acknowledge KPMP ([Citing KPMP data](https://www.kpmp.org/help-docs/citing-kpmp-data)):

	> The results here are in whole or part based upon data generated by the Kidney Precision Medicine Project. Accessed Month Day, Year. https://www.kpmp.org.
	>
	> The Kidney Precision Medicine Project (KPMP) is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) through the following grants: U01DK133081, U01DK133091, U01DK133092, U01DK133093, U01DK133095, U01DK133097, U01DK114866, U01DK114908, U01DK133090, U01DK133113, U01DK133766, U01DK133768, U01DK114907, U01DK114920, U01DK114923, U01DK114933, U24DK114886, UH3DK114926, UH3DK114861, UH3DK114915, UH3DK114937, 1U01DK144965-01, 1U01DK144994-01. We gratefully acknowledge the essential contributions of our patient participants and the support of the American public through their tax dollars.

	## License

	[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/), non-commercial academic research only. The
	KPMP data included remain subject to KPMP's terms of use.

	#### NB
	Computational data analysis was performed at [Leonhard Med](https://sis.id.ethz.ch/services/sensitiveresearchdata/), the secure trusted research environment at ETH Zurich. Our pipeline aligns with the specific cluster requirements and resources.