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https://github.com/mahmoodlab/hest
HEST: Bringing Spatial Transcriptomics and Histopathology together - NeurIPS 2024 (Spotlight)
https://github.com/mahmoodlab/hest
computational-pathology histology spatial-transcriptomics
Last synced: 2 days ago
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HEST: Bringing Spatial Transcriptomics and Histopathology together - NeurIPS 2024 (Spotlight)
- Host: GitHub
- URL: https://github.com/mahmoodlab/hest
- Owner: mahmoodlab
- License: other
- Created: 2024-03-04T18:56:54.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-14T11:03:02.000Z (17 days ago)
- Last Synced: 2024-12-22T02:07:08.280Z (9 days ago)
- Topics: computational-pathology, histology, spatial-transcriptomics
- Language: Python
- Homepage:
- Size: 36.2 MB
- Stars: 189
- Watchers: 4
- Forks: 15
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-pathology - HEST - Bringing spatial transcriptomics and histopathology together. (Data / Datasets)
README
# HEST-Library: Bringing Spatial Transcriptomics and Histopathology together
## Designed for querying and assembling HEST-1k dataset
\[ [arXiv](https://arxiv.org/abs/2406.16192) | [Data](https://huggingface.co/datasets/MahmoodLab/hest) | [Documentation](https://hest.readthedocs.io/en/latest/) | [Tutorials](https://github.com/mahmoodlab/HEST/tree/main/tutorials) | [Cite](https://github.com/mahmoodlab/hest?tab=readme-ov-file#citation) \]
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](https://faisal.ai/) at Harvard Medical School and Brigham and Women's Hospital.
### What does this repository provide?
- **HEST-1k:** Free access to HEST-1K, 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 ([email protected]).
- **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](https://github.com/mahmoodlab/HEST/blob/main/tutorials/5-Batch-effect-visualization.ipynb).
## Download/Query HEST-1k (>1TB)
To download/query HEST-1k, follow the tutorial [1-Downloading-HEST-1k.ipynb](https://github.com/mahmoodlab/HEST/blob/main/tutorials/1-Downloading-HEST-1k.ipynb) or follow instructions on [Hugging Face](https://huggingface.co/datasets/MahmoodLab/hest).
**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](https://docs.rapids.ai/install) 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,
```python
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](https://github.com/mahmoodlab/HEST/tree/main/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](https://github.com/mahmoodlab/HEST/tree/main/tutorials/3-Assembling-HEST-Data.ipynb): Walkthrough to transform a Visum sample into HEST.
- [5-Batch-effect-visualization.ipynb](https://github.com/mahmoodlab/HEST/blob/main/tutorials/5-Batch-effect-visualization.ipynb): Batch effect visualization and correction (MNN, Harmony, ComBat).
In addition, we provide complete [documentation](https://hest.readthedocs.io/en/latest/).
## 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](https://github.com/mahmoodlab/HEST/tree/main/tutorials/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.
| Model | IDC | PRAD | PAAD | SKCM | COAD | READ | ccRCC | LUAD | LYMPH IDC | Average |
|------------------------|--------|--------|--------|--------|--------|--------|--------|--------|-----------|---------|
| **[Resnet50](https://arxiv.org/abs/1512.03385)** | 0.4741 | 0.3075 | 0.3889 | 0.4822 | 0.2528 | 0.0812 | 0.2231 | 0.4917 | 0.2322 | 0.326 |
| **[CTransPath](https://www.sciencedirect.com/science/article/abs/pii/S1361841522002043)** | 0.511 | 0.3427 | 0.4378 | 0.5106 | 0.2285 | 0.11 | 0.2279 | 0.4985 | 0.2353 | 0.3447 |
| **[Phikon](https://huggingface.co/owkin/phikon)** | 0.5327 | 0.342 | 0.4432 | 0.5355 | 0.2585 | 0.1517 | 0.2423 | 0.5468 | 0.2373 | 0.3656 |
| **[CONCH](https://huggingface.co/MahmoodLab/CONCH)** | 0.5363 | 0.3548 | 0.4475 | 0.5791 | 0.2533 | 0.1674 | 0.2179 | 0.5312 | 0.2507 | 0.3709 |
| **[Remedis](https://arxiv.org/abs/2205.09723)** | 0.529 | 0.3471 | 0.4644 | 0.5818 | 0.2856 | 0.1145 | 0.2647 | 0.5336 | 0.2473 | 0.3742 |
| **[Gigapath](https://huggingface.co/prov-gigapath/prov-gigapath)** | 0.5508 | _0.3708_ | 0.4768 | 0.5538 | _0.301_ | 0.186 | 0.2391 | 0.5399 | 0.2493 | 0.3853 |
| **[UNI](https://huggingface.co/MahmoodLab/UNI)** | 0.5702 | 0.314 | 0.4764 | 0.6254 | 0.263 | 0.1762 | 0.2427 | 0.5511 | 0.2565 | 0.3862 |
| **[Virchow](https://huggingface.co/paige-ai/Virchow)** | 0.5702 | 0.3309 | 0.4875 | 0.6088 | **0.311** | 0.2019 | 0.2637 | 0.5459 | 0.2594 | 0.3977 |
| **[Virchow2](https://huggingface.co/paige-ai/Virchow2)** | 0.5922 | 0.3465 | 0.4661 | 0.6174 | 0.2578 | 0.2084 | **0.2788** | **0.5605** | 0.2582 | 0.3984 |
| **UNIv1.5** | **0.5989** | 0.3645 | _0.4902_ | _0.6401_ | 0.2925 | _0.2240_ | 0.2522 | _0.5586_ | **0.2597** | _0.4090_ |
| **[Hoptimus0](https://github.com/bioptimus/releases/blob/main/models/h-optimus/v0/LICENSE.md)** | _0.5982_ | **0.385** | **0.4932** | **0.6432** | 0.2991 | **0.2292** | _0.2654_ | 0.5582 | _0.2595_ | **0.4146** |
### Benchmarking your own model
Our tutorial in [4-Running-HEST-Benchmark.ipynb](https://github.com/mahmoodlab/HEST/tree/main/tutorials/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 `[email protected]` (and cc `[email protected]`).
- 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,
}
```
