README.md

metadata

pretty_name: GHIST+ Data and Model Bundle
license: other
tags:
  - spatial-transcriptomics
  - computational-pathology
  - xenium

GHIST+ data and model bundle

This bundle contains the model artifacts, predictions, evaluation inputs, comparison outputs, and plot-ready tables released with GHIST+. Source code is provided in that repository.

Download

hf download SydneyBioX/GHIST-Plus-bundle \
  --repo-type dataset \
  --local-dir bundle

The bundle is approximately 38 GB. Individual files can also be downloaded from this page.

Use with the figure notebooks

These notebooks regenerate figures from released expression predictions and analysis tables. Training and checkpoint reconstruction are not required.

Complete the GitHub installation and notebook setup, then run:

conda activate model_env
cd GHIST_plus
export GHIST_BUNDLE_ROOT="/path/to/bundle"
python -m jupyterlab

Set GHIST_BUNDLE_ROOT to the downloaded bundle directory containing GHIST_plus/, evaluation_data/, figure_data/, and other_models/.

Open the desired Figure3.ipynb–Figure5.ipynb, select GHIST+ (model_env), and choose Restart Kernel and Run All. Figures appear inside the notebook; save the notebook to keep its outputs.

Model checkpoints

The four released GHIST+ checkpoint files are:

• GHIST_plus/models/breast_multi/ghist_plus_breast_multi_checkpoint.pth
• GHIST_plus/models/breast_single/ghist_plus_breast_single_checkpoint.pth
• GHIST_plus/models/imputation/ghist_plus_gene_imputation_checkpoint.pth
• GHIST_plus/models/pancancer/ghist_plus_pancancer_checkpoint.pth

They exclude the frozen third-party UNI2-H encoder weights. Users must obtain UNI2-H directly from MahmoodLab/UNI2-h, accept its terms, and follow the Pretrained Checkpoints instructions in the GHIST+ README. That procedure reconstructs the complete checkpoints locally at the same filenames, so the existing inference commands and configs remain unchanged.

The bundle does not redistribute UNI2-H weights.

Contents

• Figure 3: evaluation data, GHIST+ predictions, and comparison-model predictions under evaluation_data/, GHIST_plus/predictions/, and other_models/.

• Figure 4: imputation inputs and predictions, including the bundled VQ/composition ablation predictions.

• Figure 5: paired PCC and coverage tables under figure_data/figure5/.

  bundle/ ├── GHIST_plus/ │ ├── models/ │ └── predictions/ ├── evaluation_data/ ├── figure_data/ └── other_models/

The paths and lightweight input schemas were checked against the released Figure 3–5 notebooks.

Data and third-party terms

The bundle combines author-generated artifacts, processed public source data, and outputs from comparison methods. It therefore has no single blanket license; the Hugging Face license field is other. See THIRD_PARTY_NOTICES.md for component-specific sources, versions, attributions, and terms. Upstream terms remain applicable.

Tutorial data

tutorial.ipynb does not use this bundle as its DATA_ROOT. The tutorial requires a separately prepared GHIST data directory containing aligned H&E images, segmentation masks, nuclei metadata, and inputs for the selected training mode.