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

# Dataset card for TCGA digital spatial transcriptomics data

## What is TCGA digital spatial transcriptomic?
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 1 792 TCGA spatial transcriptomic samples with over 37 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 transcriptomic for:
  - TCGA SKCM
    - 472 FF slides
    - 276 FFPE slides
  - TCGA KIRC
    - 528 FF slides
    - 516 FFPE slides


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

## How to start?

```
pip install datasets
```

#### Logging

```
from huggingface_hub import login
login(token="YOUR HUGGINGFACE TOKEN")
```

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

```
import datasets

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

# Note that the full dataset is around 2TB of data

dataset = datasets.load_dataset(
    'kalin0218/TCGA_digital_spatial_transcriptomics', 
    cache_dir=local_dir,
    patterns='*'
)
```

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

```
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"]

dataset = datasets.load_dataset(
    'kalin0218/TCGA_digital_spatial_transcriptomics', 
    cache_dir=local_dir,
    patterns=cancer_type
)
```

###  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 = 1358 × 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.

  .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

### How to cite: 

```
tbd
```