---
license: mit
language:
  - en
tags:
  - spatial-transcriptomics
  - spatial-domain-identification
  - gene-embeddings
  - DLPFC
  - single-cell
pretty_name: GreS Resources (Gene Embeddings + DLPFC Spatial Data)
size_categories:
  - 100MB<n<1GB
---

# GreS: Resources for Semantic-Guided Spatial Domain Identification

This repository hosts the resources needed to run **[GreS](https://github.com/ai4nucleome/GreS)**, a graph-based framework that incorporates gene-level semantic priors into spatial representation learning for spatial domain identification.

It contains two parts:

1. **`embeddings/`** — pretrained gene embeddings and vocabulary used to build per-spot semantic descriptors.
2. **`DLPFC/`** — example 10x Visium spatial transcriptomics data (human dorsolateral prefrontal cortex).

## Contents

```
ylu99/Gres/
├── embeddings/
│   ├── pretrained_gene_embeddings.pt   # pretrained gene embedding matrix
│   └── vocab.json                      # gene -> index vocabulary
└── DLPFC/
    ├── 151507/
    │   ├── metadata.tsv                # per-spot annotations (incl. layer labels)
    │   ├── 151507_truth.txt            # ground-truth layer labels
    │   └── spatial/                    # tissue positions + scale factors
    ├── 151508/
    └── ...                             # 12 samples in total
```

### `embeddings/`

* **`pretrained_gene_embeddings.pt`**: pretrained semantic embeddings for genes, aggregated to the spot level (weighted by expression) to form each spot's semantic descriptor.
* **`vocab.json`**: maps gene symbols to embedding indices so genes can be aligned to the embedding matrix.

### `DLPFC/`

The DLPFC dataset comprises **12 tissue sections** (`151507`–`151510`, `151669`–`151676`) from the human dorsolateral prefrontal cortex, a widely used benchmark for spatial domain identification with manually annotated cortical layers (layers 1–6 and white matter).

Each sample folder contains:

* **`metadata.tsv`**: per-spot metadata, including the ground-truth layer annotation (`layer_guess_reordered`).
* **`<id>_truth.txt`**: ground-truth labels.
* **`spatial/`**: `tissue_positions_list.csv` and `scalefactors_json.json` for spatial coordinates.

> Note: the raw gene-expression matrices (`filtered_feature_bc_matrix.h5`) and full-resolution tissue images are **not** included here due to size. Obtain them from the original [spatialLIBD / 10x Genomics](http://spatial.libd.org/spatialLIBD/) release and place them next to the provided files.

## Usage with GreS

Download the resources and place them under the GreS project tree:

```bash
# Gene embeddings  ->  GreS/embedding/text_embedd_large/
huggingface-cli download ylu99/Gres --repo-type dataset \
  --include "embeddings/*" --local-dir ./gres_resources

# Example DLPFC data  ->  GreS/data/raw_h5ad/<dataset_id>/
huggingface-cli download ylu99/Gres --repo-type dataset \
  --include "DLPFC/*" --local-dir ./gres_resources
```

Then follow the three-step pipeline in the [GreS repository](https://github.com/ai4nucleome/GreS):

1. `preprocess/generate_data.py` — build the graph-augmented `data.h5ad`.
2. `preprocess/generate_raw_gene_concat_spot_embedding.py` — build per-spot semantic embeddings.
3. `tools/train.py` — train and cluster.

See the project [`tutorial.ipynb`](https://github.com/ai4nucleome/GreS/blob/main/tutorial.ipynb) for an end-to-end walkthrough.

## License

Released under the MIT License. The DLPFC data originates from the spatialLIBD project; please also cite the original data source when using it.