Datasets:

ratschlab
/

KPMP_Xenium_virtual_spatial_transcriptomics

---
pretty_name: KPMP Xenium Virtual Spatial Transcriptomics
language:
- en
tags:
- spatial-transcriptomics
- histology
- pathology
- transcriptomics
- machine-learning
- KPMP
- kidney
- nephrology
- kidney-disease
- gene-expression
- computational-pathology
- xenium
- virtual-spatial-transcriptomics
- foundation-model
- deep-learning
- single-cell
size_categories:
- n<1K
task_categories:
- image-feature-extraction
license: cc-by-nc-sa-4.0
configs:
- config_name: default
  data_files: metadata.csv
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 Xenium virtual spatial transcriptomics

Virtual spatial transcriptomics for the **49 H&E sections** of the 10x Xenium kidney biopsies of the
[Kidney Precision Medicine Project (KPMP)](https://www.kpmp.org), one section per participant: **19,338 genes** in each
of **1,637,759 cells**, predicted with **DeepSpot-M Kidney** adapted to these sections on their measured Xenium panel
genes. Each file also holds the measured Xenium panel of the section.

**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 | hKidney sections | hMulti sections | Participants | Cells |
|---|---:|---:|---:|---:|
| AKI | 6 | – | 6 | 238,177 |
| CKD | 9 | 22 | 31 | 1,140,540 |
| DM-R | 8 | – | 8 | 218,824 |
| Healthy Reference | 1 | 3 | 4 | 40,218 |
| **Total** | **24** | **25** | **49** | **1,637,759** |

```
data/<ENROLLMENT_GROUP>/<sample_id>.h5ad    one AnnData file per section, named by its KPMP specimen id (52.0 GB in total)
metadata.csv                                one row per section
```

## Example

![KPMP Xenium CKD biopsy per cell: H&E, measured and predicted PODXL, virtual ALDOB](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_xenium_example_cells.png)

CKD biopsy (hMulti panel), per cell: H&E, measured and predicted **PODXL** (glomeruli; held out from the adaptation)
and virtual **ALDOB** (proximal tubule; not on the panel).

## 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_Xenium_virtual_spatial_transcriptomics"
# one enrollment group (leave out allow_patterns for the whole dataset)
snapshot_download(repo, repo_type="dataset", local_dir="KPMP_Xenium_data", allow_patterns="data/CKD/*")
# or select sections in metadata.csv first
meta = pd.read_csv(hf_hub_download(repo, "metadata.csv", repo_type="dataset"))
sel = meta[meta["panel"] == "hKidney_480g"]
snapshot_download(repo, repo_type="dataset", local_dir="KPMP_Xenium_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_Xenium_data/data/CKD/S-2412-005611.h5ad")
adata.X = adata.X.astype(np.float32)
sq.pl.spatial_scatter(adata, color=[None, "PODXL", "ALDOB"], size=0.3, ncols=3)
```

## Data format

- **`X`**: virtual expression of 19,338 genes per cell, log1p counts per 10,000 (log1p CP10k) on the scale of the
  Xenium panel the decoder was adapted to, stored as float16 (cast to float32 before analysis).
- **`layers['ground_truth']`**: measured Xenium expression (log1p counts per 10,000 over the panel genes), sparse,
  zero outside the panel genes.
- **`obs`**: one row per cell: `cell_id` (Xenium cell id), `sample_id` (KPMP specimen id), `participant_id`,
  `enrollment`, `panel`, `imaging_site`, centroid in full-resolution H&E pixels (`x_pixel`, `y_pixel`) and in Xenium
  micrometres (`x_um`, `y_um`).
- **`var`**: HGNC gene symbol, `ensembl_ids`, `hgnc_id`, `in_panel_hKidney` / `in_panel_hMulti` (measured on that Xenium
  panel; SLC9A3R1 and TMEM173 under their current symbols NHERF1 and STING1) and `heldout` (74 panel genes left out of
  the adaptation in every section).
- **`obsm['spatial']`** and **`uns['spatial']`**: cell centroids and an H&E overview image, as scanpy and squidpy expect.
- **`uns['he2st']`**: section and processing metadata, with the adaptation and the H&E-to-Xenium registration
  (`he_px_to_xenium_um`, 3 × 3, and its QC distance).
- **`metadata.csv`**: one row per section: file path, participant, specimen, enrollment group, panel, imaging site,
  number of cells (`n_cells`), `in_kpmp_visium_training` (participant in the KPMP Visium training data of DeepSpot-M Kidney),
  `in_kpmp_he_atlas` (participant in the
  [KPMP virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/KPMP_virtual_spatial_transcriptomics_atlas))
  and the KPMP download links of the Xenium bundle, the H&E image and the metadata file.
## Citation
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 H. and R{\"a}tsch, Gunnar},
  journal = {medRxiv},
  year    = {2026},
  doi     = {10.64898/2026.06.19.26356060},
  url     = {https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1}
}
```
## KPMP acknowledgement
The H&E images and the Xenium data 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 Xenium Virtual Spatial Transcriptomics
	language:
	- en
	tags:
	- spatial-transcriptomics
	- histology
	- pathology
	- transcriptomics
	- machine-learning
	- KPMP
	- kidney
	- nephrology
	- kidney-disease
	- gene-expression
	- computational-pathology
	- xenium
	- virtual-spatial-transcriptomics
	- foundation-model
	- deep-learning
	- single-cell
	size_categories:
	- n<1K
	task_categories:
	- image-feature-extraction
	license: cc-by-nc-sa-4.0
	configs:
	- config_name: default
	data_files: metadata.csv
	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 Xenium virtual spatial transcriptomics

	Virtual spatial transcriptomics for the 49 H&E sections of the 10x Xenium kidney biopsies of the
	[Kidney Precision Medicine Project (KPMP)](https://www.kpmp.org), one section per participant: 19,338 genes in each
	of 1,637,759 cells, predicted with DeepSpot-M Kidney adapted to these sections on their measured Xenium panel
	genes. Each file also holds the measured Xenium panel of the section.

	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 \| hKidney sections \| hMulti sections \| Participants \| Cells \|
	\|---\|---:\|---:\|---:\|---:\|
	\| AKI \| 6 \| – \| 6 \| 238,177 \|
	\| CKD \| 9 \| 22 \| 31 \| 1,140,540 \|
	\| DM-R \| 8 \| – \| 8 \| 218,824 \|
	\| Healthy Reference \| 1 \| 3 \| 4 \| 40,218 \|
	\| Total \| 24 \| 25 \| 49 \| 1,637,759 \|

	```
	data/<ENROLLMENT_GROUP>/<sample_id>.h5ad one AnnData file per section, named by its KPMP specimen id (52.0 GB in total)
	metadata.csv one row per section
	```

	## Example

	![KPMP Xenium CKD biopsy per cell: H&E, measured and predicted PODXL, virtual ALDOB](https://huggingface.co/datasets/ratschlab/deepspotm-assets/resolve/main/kpmp_xenium_example_cells.png)

	CKD biopsy (hMulti panel), per cell: H&E, measured and predicted PODXL (glomeruli; held out from the adaptation)
	and virtual ALDOB (proximal tubule; not on the panel).

	## 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_Xenium_virtual_spatial_transcriptomics"

	# one enrollment group (leave out allow_patterns for the whole dataset)
	snapshot_download(repo, repo_type="dataset", local_dir="KPMP_Xenium_data", allow_patterns="data/CKD/*")

	# or select sections in metadata.csv first
	meta = pd.read_csv(hf_hub_download(repo, "metadata.csv", repo_type="dataset"))
	sel = meta[meta["panel"] == "hKidney_480g"]
	snapshot_download(repo, repo_type="dataset", local_dir="KPMP_Xenium_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_Xenium_data/data/CKD/S-2412-005611.h5ad")
	adata.X = adata.X.astype(np.float32)
	sq.pl.spatial_scatter(adata, color=[None, "PODXL", "ALDOB"], size=0.3, ncols=3)
	```

	## Data format

	- `X`: virtual expression of 19,338 genes per cell, log1p counts per 10,000 (log1p CP10k) on the scale of the
	Xenium panel the decoder was adapted to, stored as float16 (cast to float32 before analysis).
	- `layers['ground_truth']`: measured Xenium expression (log1p counts per 10,000 over the panel genes), sparse,
	zero outside the panel genes.
	- `obs`: one row per cell: `cell_id` (Xenium cell id), `sample_id` (KPMP specimen id), `participant_id`,
	`enrollment`, `panel`, `imaging_site`, centroid in full-resolution H&E pixels (`x_pixel`, `y_pixel`) and in Xenium
	micrometres (`x_um`, `y_um`).
	- `var`: HGNC gene symbol, `ensembl_ids`, `hgnc_id`, `in_panel_hKidney` / `in_panel_hMulti` (measured on that Xenium
	panel; SLC9A3R1 and TMEM173 under their current symbols NHERF1 and STING1) and `heldout` (74 panel genes left out of
	the adaptation in every section).
	- `obsm['spatial']` and `uns['spatial']`: cell centroids and an H&E overview image, as scanpy and squidpy expect.
	- `uns['he2st']`: section and processing metadata, with the adaptation and the H&E-to-Xenium registration
	(`he_px_to_xenium_um`, 3 × 3, and its QC distance).
	- `metadata.csv`: one row per section: file path, participant, specimen, enrollment group, panel, imaging site,
	number of cells (`n_cells`), `in_kpmp_visium_training` (participant in the KPMP Visium training data of DeepSpot-M Kidney),
	`in_kpmp_he_atlas` (participant in the
	[KPMP virtual spatial transcriptomics atlas](https://huggingface.co/datasets/ratschlab/KPMP_virtual_spatial_transcriptomics_atlas))
	and the KPMP download links of the Xenium bundle, the H&E image and the metadata file.

	## Citation

	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 H. and R{\"a}tsch, Gunnar},
	journal = {medRxiv},
	year = {2026},
	doi = {10.64898/2026.06.19.26356060},
	url = {https://www.medrxiv.org/content/10.64898/2026.06.19.26356060v1}
	}
	```

	## KPMP acknowledgement

	The H&E images and the Xenium data 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.