| --- |
| 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. |
| |
