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HirenMadhu/
HEIST

Name: HEIST
Availability: InStock

pytorch

safetensors

single-cell

spatial-transcriptomics

graph-neural-network

mit

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HEIST

Pre-trained checkpoint for HEIST: Hierarchical Embeddings for Integrated Spatial Transcriptomics (ICLR 2026).

Model code, training scripts, and tutorials live on GitHub: https://github.com/Graph-and-Geometric-Learning/HEIST

Usage

Clone the repo and install dependencies:

bash

git clone https://github.com/Graph-and-Geometric-Learning/HEIST
cd HEIST
pip install -e .

Then load the pre-trained weights:

python

from model.model import GraphEncoder

model = GraphEncoder.from_pretrained("HirenMadhu/HEIST")
model.eval()

For a full embedding-extraction tutorial (preprocessing, graph construction, PHATE visualization), see cell_embeddings.ipynb in the GitHub repo.

Citation

bibtex

@inproceedings{madhu2026heist,
  title={{HEIST}: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data},
  author={Madhu, Hiren and Rocha, Jo{\~a}o Felipe and Huang, Tinglin and Viswanath, Siddharth and Krishnaswamy, Smita and Ying, Rex},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=lK82jpa8jr}
}