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

Download the entire HEST-1k dataset:

from huggingface_hub import snapshot_download

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

# Note that the full dataset is around 1TB of data
snapshot_download(repo_id="MahmoodLab/hest", repo_type='dataset', local_dir=local_dir)

Download a subset of HEST-1k:

from huggingface_hub import snapshot_download

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]
snapshot_download(repo_id="MahmoodLab/hest", repo_type='dataset', local_dir=local_dir, allow_patterns=list_patterns)

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

from huggingface_hub import snapshot_download
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_0.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]
snapshot_download(repo_id="MahmoodLab/hest", repo_type='dataset', local_dir=local_dir, allow_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.

Contact:

• Guillaume Jaume Harvard Medical School, Boston, Mahmood Lab (gjaume@bwh.harvard.edu)
• Paul Doucet Harvard Medical School, Boston, Mahmood Lab (pdoucet@bwh.harvard.edu)

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