| --- |
| 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 |
|
|
| <img src="fig1a.jpg" alt="Description" style="width: 38%;" align="right"/> |
|
|
| #### What is HEST-1k? |
|
|
| - A collection of <b>1,108</b> 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 <b>1.5 million</b> expression/morphology pairs and <b>60 million</b> 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](https://huggingface.co/join). |
|
|
| ### 2. Accept terms of use of HEST |
|
|
| 1. On this page click request access (access will be automatically granted) |
| 2. At this stage, you can already manually inspect the data by navigating in the `Files and version` |
|
|
| ### 3. Create a Hugging Face Token |
|
|
| 1. **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. |
|
|
| 2. **Access Tokens:** In the settings menu, find and click on `Access tokens`. |
|
|
| 3. **Create New Token:** |
| - Click on `New token`. |
| - Set the token name (e.g., `hest`). |
| - Set the access level to `Write`. |
| - Click on `Create`. |
|
|
| 4. **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: |
| ```python |
| 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: |
| ```python |
| 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... |
| ```python |
| 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]`: |
| ```python |
| 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](https://github.com/mahmoodlab/hest) and the [documentation](https://hest.readthedocs.io/en/latest/) 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: |
| - <b>Guillaume Jaume</b> Harvard Medical School, Boston, Mahmood Lab (`gjaume@bwh.harvard.edu`) |
| - <b>Paul Doucet</b> Harvard Medical School, Boston, Mahmood Lab (`pdoucet@bwh.harvard.edu`) |
|
|
| <i>The dataset is distributed under the Attribution-NonCommercial-ShareAlike 4.0 International license (CC BY-NC-SA 4.0 Deed)</i> |
