terra-st 0.1.3

Spatial transcriptomics foundation model

TERRA is a foundation model for spatial transcriptomics. It uses a Joint-Embedding Predictive Architecture (JEPA): cells are tokenized together with their spatial neighbors, parts of the input are masked, and the model learns by predicting the latent representations of the masked cell and neighborhood tokens. The resulting embeddings capture both a cell's own expression and its tissue microenvironment.

Pretrained on HST-Corpus-112M (>100M cells at single-cell resolution spanning human spatial-transcriptomics datasets), TERRA produces cell- and neighborhood-level embeddings that transfer to downstream tasks such as niche and cell-type identification, batch-integrated atlasing, spatial gene-pair scoring, and in-silico perturbation — without task-specific retraining.

• 📖 Documentation: https://terra-st.readthedocs.io
• 🤗 Pretrained models: https://huggingface.co/Lotfollahi-lab (TERRA-96M, TERRA-112M)
• 📓 Tutorial: end-to-end walkthrough

Key features

• Spatially-aware embeddings — cell and neighborhood representations learned in latent space via JEPA.
• Pretrained and ready to use — download a model from the Hugging Face Hub and embed your own AnnData in a few lines.
• Self-contained model bundles — each release ships the checkpoint, tokenizer, and gene-reference files needed to reproduce its training-time harmonization.
• Downstream analyses — niche/cell-type clustering, gene-pair spatial scoring, EMD-based spatial structure, and perturbation.

Installation

TERRA is published on PyPI as terra-st (the import name is terra) and requires an NVIDIA GPU. Install PyTorch first (so it matches your GPU), then TERRA — we recommend uv.

1. Install PyTorch for your hardware. Run nvidia-smi, read the "CUDA Version" in the top-right, and install the matching CUDA build (see the PyTorch install guide), e.g.:

uv pip install torch --index-url https://download.pytorch.org/whl/cu124

2. Install TERRA.

uv pip install terra-st

Plain pip install terra-st works too. For a development install from a clone of this repository (after step 1): uv pip install -e ".[dev,test,doc]".

Verify the install:

python -c "import torch; print(torch.__version__, torch.version.cuda, torch.cuda.is_available())"

Quickstart

Download a pretrained model and embed your own spatial AnnData with the end-to-end pipeline. Each downloaded bundle contains the gene-reference files needed for harmonization, so no external paths are required:

import anndata as ad
from terra import download_pretrained, harmonize_tokenize_embed_pipeline

adata = ad.read_h5ad("my_spatial_data.h5ad")   # raw counts in adata.X

model_dir = download_pretrained("Lotfollahi-lab/TERRA-96M")

adata = harmonize_tokenize_embed_pipeline(
    adata=adata,
    sample_key="sample",            # column in adata.obs identifying samples
    batch_key="batch",              # column to store the batch identifier
    model_folder_path=model_dir,
    cache_directory_path="./terra_cache",
)

# Cell- and neighborhood-level embeddings are now in adata.obsm.

See the documentation for the step-by-step pipeline, downstream analyses (niche identification, gene-pair scoring, perturbation), and the full tutorial.

Citation

If you use TERRA in your research, please cite the manuscript (in preparation). A BibTeX entry and DOI will be added here on publication.

License

The TERRA code is released under the BSD 3-Clause License. Pretrained model weights distributed on the Hugging Face Hub are released under CC-BY-NC-4.0 (non-commercial use).