metadata

license: cc-by-nc-sa-4.0
language:
  - en
  - vi
  - aa
  - an
tags:
  - spatial-transcriptomics
  - histology
  - pathology
task_categories:
  - image-classification
  - feature-extraction
  - image-segmentation
  - table-question-answering
  - text-classification
  - text-to-speech
size_categories:
  - 100M<n<1B

Model Card for HEST-1k

What is HEST-1k?

A collection of 1,108 spatial transcriptomic profiles, each linked and aligned to a Whole Slide Image (with pixel size > 1.15 µm/px) and metadata.
HEST-1k was assembled from 131 public and internal cohorts encompassing:
- 25 organs
- 2 species (Homo Sapiens and Mus Musculus)
- 320 cancer samples from 25 cancer types.

HEST-1k processing enabled the identification of 1.5 million expression/morphology pairs and 60 million nuclei

Instructions for Setting Up HuggingFace Account and Token

1. Create an Account on HuggingFace

Follow the instructions provided on the HuggingFace sign-up page.

2. Accept terms of use of HEST

On this page click request access (access will be automatically granted)
At this stage, you can already manually inspect the data by navigating in the Files and version

3. Create a Hugging Face Token

Go to Settings: Navigate to your profile settings by clicking on your profile picture in the top right corner and selecting Settings from the dropdown menu.
Access Tokens: In the settings menu, find and click on Access tokens.
Create New Token:
- Click on New token.
- Set the token name (e.g., hest).
- Set the access level to Write.
- Click on Create.
Copy Token: After the token is created, copy it to your clipboard. You will need this token for authentication.

4. Logging

Run the following

pip install datasets

from huggingface_hub import login

login(token="YOUR HUGGINGFACE TOKEN")

Download the entire HEST-1k dataset:

import datasets

local_dir='hest_data' # hest will be dowloaded to this folder

# Note that the full dataset is around 1TB of data

dataset = datasets.load_dataset(
    'MahmoodLab/hest', 
    cache_dir=local_dir,
    patterns='*'
)

Download a subset of HEST-1k:

import datasets

local_dir='hest_data' # hest will be dowloaded to this folder

ids_to_query = ['TENX96', 'TENX99'] # list of ids to query

list_patterns = [f"*{id}[_.]**" for id in ids_to_query]
dataset = datasets.load_dataset(
    'MahmoodLab/hest', 
    cache_dir=local_dir,
    patterns=list_patterns
)

Query HEST by organ, techonology, oncotree code...

import datasets
import pandas as pd

local_dir='hest_data' # hest will be dowloaded to this folder

meta_df = pd.read_csv("hf://datasets/MahmoodLab/hest/HEST_v1_0_2.csv")

# Filter the dataframe by organ, oncotree code...
meta_df = meta_df[meta_df['oncotree_code'] == 'IDC']
meta_df = meta_df[meta_df['organ'] == 'Breast']

ids_to_query = meta_df['id'].values

list_patterns = [f"*{id}[_.]**" for id in ids_to_query]
dataset = datasets.load_dataset(
    'MahmoodLab/hest', 
    cache_dir=local_dir,
    patterns=list_patterns
)

Loading the data with the python library `hest`

Once downloaded, you can then easily load the dataset as a List[HESTData]:

from hest import load_hest


print('load hest...')
hest_data = load_hest('hest_data') # location of the data
print('loaded hest')
for d in hest_data:
    print(d)

Please visit the github repo and the documentation for more information about the hest library API.

Data organization

For each sample:

wsis/: H&E stained Whole Slide Images in pyramidal Generic TIFF (or pyramidal Generic BigTIFF if >4.1GB)
st/: spatial transcriptomics expressions in a scanpy .h5ad object
metadata/: metadata
spatial_plots/: overlay of the WSI with the st spots
thumbnails/: downscaled version of the WSI
tissue_seg/: tissue segmentation masks:
- {id}_mask.jpg: downscaled or full resolution greyscale tissue mask
- {id}_mask.pkl: tissue/holes contours in a pickle file
- {id}_vis.jpg: visualization of the tissue mask on the downscaled WSI
cellvit_seg/: cellvit nuclei segmentation
pixel_size_vis/: visualization of the pixel size
patches/: 256x256 H&E patches (0.5µm/px) extracted around ST spots in a .h5 object optimized for deep-learning. Each patch is matched to the corresponding ST profile (see st/) with a barcode.
patches_vis/: visualization of the mask and patches on a downscaled WSI.

How to cite:

@article{jaume2024hest,
    author = {Jaume, Guillaume and Doucet, Paul and Song, Andrew H. and Lu, Ming Y. and Almagro-Perez, Cristina and Wagner, Sophia J. and Vaidya, Anurag J. and Chen, Richard J. and Williamson, Drew F. K. and Kim, Ahrong and Mahmood, Faisal},
    title = {{HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis}},
    journal = {arXiv},
    year = {2024},
    month = jun,
    eprint = {2406.16192},
    url = {https://arxiv.org/abs/2406.16192v1}
}

Contact:

Guillaume Jaume Harvard Medical School, Boston, Mahmood Lab ([email protected])
Paul Doucet Harvard Medical School, Boston, Mahmood Lab ([email protected])

The dataset is distributed under the Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0 Deed)