Datasets:

nonchev
/

TCGA_virtual_spatial_transcriptomics

---
language:
- en
tags:
- spatial-transcriptomics
- histology
- pathology
- transcriptomics
- machine-learning
size_categories:
- 1K<n<10K
license: cc-by-nc-nd-4.0

extra_gated_heading: "Acknowledge license to accept the repository"

extra_gated_description: >
  This dataset is licensed under CC-BY-NC-ND-4.0.
  Commercial use is not permitted unless explicit written permission
  is granted by the authors. If you are requesting access on behalf of
  a commercial entity, please clearly explain your intended use.
  By agreeing, you accept to share your contact information (email and username)
  with the repository authors and confirm that you will not use the dataset
  for harmful, unethical, or malicious purposes. You also confirm that you
  will not distribute or share any part of the dataset. If another user within
  your organization wishes to access the dataset, they must register as an
  individual user and agree to comply with the terms of use. Please allow
  3-5 business days for our team to process your request.
extra_gated_fields:
  Full name (first and last): text
  Current affiliation (no abbreviations): text
  Type of Affiliation: text
  Current and official institutional email:
    type: text
    help: "This must match your primary email in your Hugging Face account. Emails from gmail, hotmail, and qq domains will be denied."
  Please explain your intended research use: text
  I confirm that I have read and understood the dataset license terms: checkbox
  I agree not to distribute the dataset:
    type: checkbox
    help: "If another user within my organization wishes to use the dataset, they must register as an individual user."
  I agree to receive news and updates about this technology: checkbox
---

# Dataset card for TCGA digital spatial transcriptomics data

This repository contains results from the paper "DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H\&E Images".

**Authors**: Kalin Nonchev, Sebastian Dawo, Karina Selina, Holger Moch, Sonali Andani, Tumor Profiler Consortium, Viktor Hendrik Koelzer, and Gunnar Rätsch

The preprint is available [here](https://www.medrxiv.org/content/10.1101/2025.02.09.25321567v1).

## What is TCGA digital spatial transcriptomics?
We trained a model using available spatial transcriptomics data to predict gene expression for both fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) slides from TCGA SKCM (skin melanoma) and TCGA KIRC (kidney cancer) datasets. More information can be found at: https://github.com/ratschlab/DeepSpot.

![Graphical summary](summary.jpg) 
**Fig:** DeepSpot predicts spatial transcriptomics from H&E images by leveraging recent foundation models in pathology and spatial multi-level tissue context. 1: DeepSpot is trained to predict 5 000 genes, with hyperparameters optimized using cross-validation. 2: DeepSpot
can be used for de novo spatial transcriptomics prediction or for correcting existing spatial transcriptomics data. 3: Validation involves nested leave-one-out patient cross-validation and
out-of-distribution testing. We predicted spatial transcriptomics from TCGA slide images, aggregated the data into pseudo-bulk RNA profiles, and compared them with the available
ground truth bulk RNA-seq. 4: DeepSpot generated 3 780 TCGA spatial transcriptomics samples with over 56 million spots from melanoma or kidney cancer patients, enriching the
available spatial transcriptomics data for TCGA samples and providing valuable insights into the molecular landscapes of cancer tissues.


The data includes spatial transcriptomics for:
  - TCGA SKCM
    - 472 FF slides
    - 276 FFPE slides
  - TCGA KIRC
    - 528 FF slides
    - 516 FFPE slides
  - TCGA LUSC
    - 455 FF slides
    - 471 FFPE slides 
  - TCGA LUAD
    - 537 FF slides
    - 525 FFPE slides


Folder tree:
```
HF
├── TCGA_LUAD
│   ├── FF
│   └── FFPE
├── TCGA_LUSC
│   ├── FF
│   └── FFPE
├── TCGA_KIRC
│   ├── FF
│   └── FFPE
└── TCGA_SKCM
    ├── FF
    └── FFPE
```

## How to start?

```
pip install datasets
```

#### Logging

```
from huggingface_hub import login, hf_hub_download, snapshot_download
import squidpy as sq
import pandas as pd
import scanpy as sc
import datasets
login(token="YOUR HUGGINGFACE TOKEN")
```

#### Load metadata information

```
# Define dataset details
repo_id = "nonchev/TCGA_digital_spatial_transcriptomics"
filename = "metadata_2025-01-11.csv"
```

```
# Create path
file_path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
# Load metata
metadata = pd.read_csv(file_path)
metadata.head()
```

```
        dataset slide_type                                          sample_id  n_spots                                          file_path
0     TCGA_SKCM       FFPE  TCGA-BF-AAP6-01Z-00-DX1.EFF1D6E1-CDBC-4401-A10...     5860  TCGA_SKCM/FFPE/TCGA-BF-AAP6-01Z-00-DX1.EFF1D6E...
1     TCGA_SKCM       FFPE  TCGA-FS-A1ZU-06Z-00-DX3.0C477EE6-C085-42BE-8BA...     2856  TCGA_SKCM/FFPE/TCGA-FS-A1ZU-06Z-00-DX3.0C477EE...
2     TCGA_SKCM       FFPE  TCGA-D9-A1X3-06Z-00-DX1.17AC16CC-5B22-46B3-B9C...     6236  TCGA_SKCM/FFPE/TCGA-D9-A1X3-06Z-00-DX1.17AC16C...
```

### Download a single TCGA spatial transcriptomics sample

```
local_dir = 'TCGA_data'  # Change the folder path as needed
snapshot_download("nonchev/TCGA_digital_spatial_transcriptomics", 
                  local_dir=local_dir,
                  allow_patterns="TCGA_SKCM/FFPE/TCGA-D9-A3Z3-06Z-00-DX1.C4820632-C64D-4661-94DD-9F27F75519C3.h5ad.gz",
                  repo_type="dataset")
```

```
adata = sc.read_h5ad("path/to/h5ad.gz")
sq.pl.spatial_scatter(adata, 
                      color=["SOX10", "CD37", "COL1A1", "predicted_label"],
                      size=20, img_alpha=0.8, ncols=2)
```
![example](tcga_example.png)


#### Download the entire TCGA digital spatial transcriptomics data

```
local_dir = 'TCGA_data'  # Change the folder path as needed
# Note that the full dataset is around 2TB
snapshot_download("nonchev/TCGA_digital_spatial_transcriptomics", 
                  local_dir=local_dir,
                  repo_type="dataset")
```

#### Download subset of TCGA digital spatial transcriptomics: 

```
import datasets
local_dir='TCGA_data' # will be downloaded to this folder
cancer_type = ['TCGA_KIRC/*'] # OR TCGA_SKCM
## or ['TCGA_KIRC/FF/*'] or ['TCGA_KIRC/FFPE/*'] or based on slide type ["*/FFPE/*", "*/FF/*"]
snapshot_download("nonchev/TCGA_digital_spatial_transcriptomics", 
                  local_dir=local_dir,
                  allow_patterns=cancer_type,
                  repo_type="dataset")
```

###  Data organization

Each file is of the form {slide_id}.h5ad.gz and can be loaded as:
```
import scanpy as sc
# Load the data
adata = sc.read_h5ad("../path/to/slide_id.h5ad.gz") 
# Note: Since the data is compressed, loading it may take more time.
# It is recommended to uncompress the data if sufficient storage is available.
adata
```
```
AnnData object with n_obs × n_vars = 4428 × 5000
    obs: 'x_array', 'y_array', 'x_pixel', 'y_pixel', 'barcode', 'predicted_label'
    uns: '20x_slide', 'scaled_slide_info', 'spatial'
    obsm: 'spatial'
```
where:

  .obs
  - x_array and y_array represent the spot coordinates on the image grid.
  - x_pixel and y_pixel correspond to the center spot coordinates on the slide, scaled to 20x magnification.
  - predicted_label is the label transferred from the training data, obtained by fitting a random forest model on the provided manual annotations for Melanoma or the cluster labels for Kidney Cancer.
  - the remaining columns with label names represent the probabilities of the spot being assigned to the predicted_label.
  - TCGA KIRC FFPE has additional expert_annotation (TLS/Normal tissue) based on https://www.nature.com/articles/s43856-023-00421-7
  .var
  - 20x_slide - original slide image downloaded from TCGA and scaled to 20x magnification
  - scaled_slide_info metadata
  - spatial - metadata required for squidpy.pl.spatial_scatter
#### NB: To distinguish in-tissue spots from the background, tiles with a mean RGB value above 200 (near white) were discarded. Additional preprocessing can remove potential image artifacts.
### How to cite: 
```
@article{nonchev2025deepspot,
  title={DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H\&E Images},
  author={Nonchev, Kalin and Dawo, Sebastian and Silina, Karina and Moch, Holger and Andani, Sonali and Tumor Profiler Consortium and Koelzer, Viktor H and Raetsch, Gunnar},
  journal={medRxiv},
  pages={2025--02},
  year={2025},
  publisher={Cold Spring Harbor Laboratory Press}
}
```
#### NB: Computational data analysis was performed at Leonhard Med (https://sis.id.ethz.ch/services/sensitiveresearchdata/) secure trusted research environment at ETH Zurich. Our pipeline aligns with the specific cluster requirements and resources.

	---
	language:
	- en
	tags:
	- spatial-transcriptomics
	- histology
	- pathology
	- transcriptomics
	- machine-learning
	size_categories:
	- 1K<n<10K
	license: cc-by-nc-nd-4.0

	extra_gated_heading: "Acknowledge license to accept the repository"

	extra_gated_description: >
	This dataset is licensed under CC-BY-NC-ND-4.0.
	Commercial use is not permitted unless explicit written permission
	is granted by the authors. If you are requesting access on behalf of
	a commercial entity, please clearly explain your intended use.
	By agreeing, you accept to share your contact information (email and username)
	with the repository authors and confirm that you will not use the dataset
	for harmful, unethical, or malicious purposes. You also confirm that you
	will not distribute or share any part of the dataset. If another user within
	your organization wishes to access the dataset, they must register as an
	individual user and agree to comply with the terms of use. Please allow
	3-5 business days for our team to process your request.

	extra_gated_fields:
	Full name (first and last): text
	Current affiliation (no abbreviations): text
	Type of Affiliation: text
	Current and official institutional email:
	type: text
	help: "This must match your primary email in your Hugging Face account. Emails from gmail, hotmail, and qq domains will be denied."
	Please explain your intended research use: text
	I confirm that I have read and understood the dataset license terms: checkbox
	I agree not to distribute the dataset:
	type: checkbox
	help: "If another user within my organization wishes to use the dataset, they must register as an individual user."
	I agree to receive news and updates about this technology: checkbox
	---


	# Dataset card for TCGA digital spatial transcriptomics data

	This repository contains results from the paper "DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H\&E Images".

	Authors: Kalin Nonchev, Sebastian Dawo, Karina Selina, Holger Moch, Sonali Andani, Tumor Profiler Consortium, Viktor Hendrik Koelzer, and Gunnar Rätsch

	The preprint is available [here](https://www.medrxiv.org/content/10.1101/2025.02.09.25321567v1).

	## What is TCGA digital spatial transcriptomics?
	We trained a model using available spatial transcriptomics data to predict gene expression for both fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) slides from TCGA SKCM (skin melanoma) and TCGA KIRC (kidney cancer) datasets. More information can be found at: https://github.com/ratschlab/DeepSpot.

	![Graphical summary](summary.jpg)
	Fig: DeepSpot predicts spatial transcriptomics from H&E images by leveraging recent foundation models in pathology and spatial multi-level tissue context. 1: DeepSpot is trained to predict 5 000 genes, with hyperparameters optimized using cross-validation. 2: DeepSpot
	can be used for de novo spatial transcriptomics prediction or for correcting existing spatial transcriptomics data. 3: Validation involves nested leave-one-out patient cross-validation and
	out-of-distribution testing. We predicted spatial transcriptomics from TCGA slide images, aggregated the data into pseudo-bulk RNA profiles, and compared them with the available
	ground truth bulk RNA-seq. 4: DeepSpot generated 3 780 TCGA spatial transcriptomics samples with over 56 million spots from melanoma or kidney cancer patients, enriching the
	available spatial transcriptomics data for TCGA samples and providing valuable insights into the molecular landscapes of cancer tissues.


	The data includes spatial transcriptomics for:
	- TCGA SKCM
	- 472 FF slides
	- 276 FFPE slides
	- TCGA KIRC
	- 528 FF slides
	- 516 FFPE slides
	- TCGA LUSC
	- 455 FF slides
	- 471 FFPE slides
	- TCGA LUAD
	- 537 FF slides
	- 525 FFPE slides


	Folder tree:
	```
	HF
	├── TCGA_LUAD
	│ ├── FF
	│ └── FFPE
	├── TCGA_LUSC
	│ ├── FF
	│ └── FFPE
	├── TCGA_KIRC
	│ ├── FF
	│ └── FFPE
	└── TCGA_SKCM
	├── FF
	└── FFPE
	```

	## How to start?

	```
	pip install datasets
	```

	#### Logging

	```
	from huggingface_hub import login, hf_hub_download, snapshot_download
	import squidpy as sq
	import pandas as pd
	import scanpy as sc
	import datasets


	login(token="YOUR HUGGINGFACE TOKEN")
	```

	#### Load metadata information

	```
	# Define dataset details
	repo_id = "nonchev/TCGA_digital_spatial_transcriptomics"
	filename = "metadata_2025-01-11.csv"
	```

	```
	# Create path
	file_path = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
	# Load metata
	metadata = pd.read_csv(file_path)
	metadata.head()
	```

	```
	dataset slide_type sample_id n_spots file_path
	0 TCGA_SKCM FFPE TCGA-BF-AAP6-01Z-00-DX1.EFF1D6E1-CDBC-4401-A10... 5860 TCGA_SKCM/FFPE/TCGA-BF-AAP6-01Z-00-DX1.EFF1D6E...
	1 TCGA_SKCM FFPE TCGA-FS-A1ZU-06Z-00-DX3.0C477EE6-C085-42BE-8BA... 2856 TCGA_SKCM/FFPE/TCGA-FS-A1ZU-06Z-00-DX3.0C477EE...
	2 TCGA_SKCM FFPE TCGA-D9-A1X3-06Z-00-DX1.17AC16CC-5B22-46B3-B9C... 6236 TCGA_SKCM/FFPE/TCGA-D9-A1X3-06Z-00-DX1.17AC16C...
	```

	### Download a single TCGA spatial transcriptomics sample

	```
	local_dir = 'TCGA_data' # Change the folder path as needed

	snapshot_download("nonchev/TCGA_digital_spatial_transcriptomics",
	local_dir=local_dir,
	allow_patterns="TCGA_SKCM/FFPE/TCGA-D9-A3Z3-06Z-00-DX1.C4820632-C64D-4661-94DD-9F27F75519C3.h5ad.gz",
	repo_type="dataset")
	```

	```
	adata = sc.read_h5ad("path/to/h5ad.gz")
	sq.pl.spatial_scatter(adata,
	color=["SOX10", "CD37", "COL1A1", "predicted_label"],
	size=20, img_alpha=0.8, ncols=2)
	```
	![example](tcga_example.png)


	#### Download the entire TCGA digital spatial transcriptomics data

	```
	local_dir = 'TCGA_data' # Change the folder path as needed

	# Note that the full dataset is around 2TB

	snapshot_download("nonchev/TCGA_digital_spatial_transcriptomics",
	local_dir=local_dir,
	repo_type="dataset")
	```

	#### Download subset of TCGA digital spatial transcriptomics:

	```
	import datasets

	local_dir='TCGA_data' # will be downloaded to this folder

	cancer_type = ['TCGA_KIRC/*'] # OR TCGA_SKCM

	## or ['TCGA_KIRC/FF/'] or ['TCGA_KIRC/FFPE/'] or based on slide type ["/FFPE/", "/FF/"]

	snapshot_download("nonchev/TCGA_digital_spatial_transcriptomics",
	local_dir=local_dir,
	allow_patterns=cancer_type,
	repo_type="dataset")
	```

	### Data organization

	Each file is of the form {slide_id}.h5ad.gz and can be loaded as:

	```
	import scanpy as sc

	# Load the data
	adata = sc.read_h5ad("../path/to/slide_id.h5ad.gz")
	# Note: Since the data is compressed, loading it may take more time.
	# It is recommended to uncompress the data if sufficient storage is available.
	adata
	```
	```
	AnnData object with n_obs × n_vars = 4428 × 5000
	obs: 'x_array', 'y_array', 'x_pixel', 'y_pixel', 'barcode', 'predicted_label'
	uns: '20x_slide', 'scaled_slide_info', 'spatial'
	obsm: 'spatial'
	```

	where:

	.obs
	- x_array and y_array represent the spot coordinates on the image grid.
	- x_pixel and y_pixel correspond to the center spot coordinates on the slide, scaled to 20x magnification.
	- predicted_label is the label transferred from the training data, obtained by fitting a random forest model on the provided manual annotations for Melanoma or the cluster labels for Kidney Cancer.
	- the remaining columns with label names represent the probabilities of the spot being assigned to the predicted_label.
	- TCGA KIRC FFPE has additional expert_annotation (TLS/Normal tissue) based on https://www.nature.com/articles/s43856-023-00421-7

	.var
	- 20x_slide - original slide image downloaded from TCGA and scaled to 20x magnification
	- scaled_slide_info metadata
	- spatial - metadata required for squidpy.pl.spatial_scatter


	#### NB: To distinguish in-tissue spots from the background, tiles with a mean RGB value above 200 (near white) were discarded. Additional preprocessing can remove potential image artifacts.


	### How to cite:

	```
	@article{nonchev2025deepspot,
	title={DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H\&E Images},
	author={Nonchev, Kalin and Dawo, Sebastian and Silina, Karina and Moch, Holger and Andani, Sonali and Tumor Profiler Consortium and Koelzer, Viktor H and Raetsch, Gunnar},
	journal={medRxiv},
	pages={2025--02},
	year={2025},
	publisher={Cold Spring Harbor Laboratory Press}
	}
	```

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