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README.md

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+---
+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.