| | --- |
| | language: |
| | - en |
| | tags: |
| | - spatial-transcriptomics |
| | - histology |
| | - pathology |
| | - transcriptomics |
| | - machine-learning |
| | size_categories: |
| | - 1K<n<10K |
| | license: cc-by-nc-nd-4.0 |
| |
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| | --- |
| | |
| |
|
| | # 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. |
| |
|
| |  |
| | **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) |
| | ``` |
| |  |
| |
|
| |
|
| | #### 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. |
