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HEST-Library: Bringing Spatial Transcriptomics and Histopathology together

Designed for querying and assembling HEST-1k dataset

[ arXiv | Data | Documentation | Tutorials | Cite ]

Welcome to the official GitHub repository of the HEST-Library introduced in "HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis", NeurIPS Spotlight, 2024. This project was developed by the Mahmood Lab at Harvard Medical School and Brigham and Women's Hospital.

What does this repository provide?

• HEST-1k: Free access to <b>HEST-1K</b>, a dataset of 1,229 paired Spatial Transcriptomics samples with HE-stained whole-slide images
• HEST-Library: A series of helpers to assemble new ST samples (ST, Visium, Visium HD, Xenium) and work with HEST-1k (ST analysis, batch effect viz and correction, etc.)
• HEST-Benchmark: A new benchmark to assess the predictive performance of foundation models for histology in predicting gene expression from morphology

HEST-1k, HEST-Library, and HEST-Benchmark are released under the Attribution-NonCommercial-ShareAlike 4.0 International license.

Updates

• 21.10.24: HEST has been accepted to NeurIPS 2024 as a Spotlight! We will be in Vancouver from Dec 10th to 15th. Send us a message if you wanna learn more about HEST (gjaume@bwh.harvard.edu).

• 23.09.24: 121 new samples released, including 27 Xenium and 7 Visium HD! We also make the aligned Xenium transcripts + the aligned DAPI segmented cells/nuclei public.

• 30.08.24: HEST-Benchmark results updated. Includes H-Optimus-0, Virchow 2, Virchow, and GigaPath. New COAD task based on 4 Xenium samples. HuggingFace bench data have been updated.

• 28.08.24: New set of helpers for batch effect visualization and correction. Tutorial here.

Download/Query HEST-1k (>1TB)

To download/query HEST-1k, follow the tutorial 1-Downloading-HEST-1k.ipynb or follow instructions on Hugging Face.

NOTE: The entire dataset weighs more than 1TB but you can easily download a subset by querying per id, organ, species...

HEST-Library installation

git clone https://github.com/mahmoodlab/HEST.git
cd HEST
conda create -n "hest" python=3.9
conda activate hest
pip install -e .

Additional dependencies (for WSI manipulation):

sudo apt install libvips libvips-dev openslide-tools

Additional dependencies (GPU acceleration):

If a GPU is available on your machine, we recommend installing cucim on your conda environment. (hest was tested with cucim-cu12==24.4.0 and CUDA 12.1)

pip install \
    --extra-index-url=https://pypi.nvidia.com \
    cudf-cu12==24.6.* dask-cudf-cu12==24.6.* cucim-cu12==24.6.* \
    raft-dask-cu12==24.6.*

NOTE: HEST-Library was only tested on Linux/macOS machines, please report any bugs in the GitHub issues.

Inspect HEST-1k with HEST-Library

You can then simply view the dataset as,

from hest import iter_hest

for st in iter_hest('../hest_data', id_list=['TENX95']):
    print(st)

HEST-Library API

The HEST-Library allows assembling new samples using HEST format and interacting with HEST-1k. We provide two tutorials:

• 2-Interacting-with-HEST-1k.ipynb: Playing around with HEST data for loading patches. Includes a detailed description of each scanpy object.
• 3-Assembling-HEST-Data.ipynb: Walkthrough to transform a Visum sample into HEST.
• 5-Batch-effect-visualization.ipynb: Batch effect visualization and correction (MNN, Harmony, ComBat).

In addition, we provide complete documentation.

HEST-Benchmark

The HEST-Benchmark was designed to assess 11 foundation models for pathology under a new, diverse, and challenging benchmark. HEST-Benchmark includes nine tasks for gene expression prediction (50 highly variable genes) from morphology (112 x 112 um regions at 0.5 um/px) in nine different organs and eight cancer types. We provide a step-by-step tutorial to run HEST-Benchmark and reproduce our results in 4-Running-HEST-Benchmark.ipynb.

HEST-Benchmark results (08.30.24)

HEST-Benchmark was used to assess 11 publicly available models. Reported results are based on a Ridge Regression with PCA (256 factors). Ridge regression unfairly penalizes models with larger embedding dimensions. To ensure fair and objective comparison between models, we opted for PCA-reduction. Model performance measured with Pearson correlation. Best is bold, second best is underlined. Additional results based on Random Forest and XGBoost regression are provided in the paper.

Benchmarking your own model

Our tutorial in 4-Running-HEST-Benchmark.ipynb will guide users interested in benchmarking their own model on HEST-Benchmark.

Note: Spontaneous contributions are encouraged if researchers from the community want to include new models. To do so, simply create a Pull Request.

Issues

• The preferred mode of communication is via GitHub issues.
• If GitHub issues are inappropriate, email gjaume@bwh.harvard.edu (and cc homedoucetpaul@gmail.com).
• Immediate response to minor issues may not be available.

Citation

If you find our work useful in your research, please consider citing:

Jaume, G., Doucet, P., Song, A. H., Lu, M. Y., Almagro-Perez, C., Wagner, S. J., Vaidya, A. J., Chen, R. J., Williamson, D. F. K., Kim, A., & Mahmood, F. HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis. Advances in Neural Information Processing Systems, December 2024.

@inproceedings{jaume2024hest,
    author = {Guillaume Jaume and Paul Doucet and Andrew H. Song and Ming Y. Lu and Cristina Almagro-Perez and Sophia J. Wagner and Anurag J. Vaidya and Richard J. Chen and Drew F. K. Williamson and Ahrong Kim and Faisal Mahmood},
    title = {HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {2024},
    month = dec,
}

<img src=docs/joint_logo.png>