scLightGAT Data

This repository contains the training and testing datasets for scLightGAT: A range-constrained Graph Attention Network for single-cell clustering and annotation.

These files are structured to be compatible with the scLightGAT project.

Dataset Structure

The dataset contains processed .h5ad files organized for the scLightGAT pipelines.

• Integrated_training/: Contains train.h5ad, the large-scale reference training set used for the DVAE and GAT models.
• Independent_testing/: Contains independent datasets used for benchmarking and inference (e.g., sapiens_full, lung_full, GSE115978, etc.).
• caf.data/: Additional data specific to Cancer-Associated Fibroblasts (CAF) experiments.

Directory Layout

When downloaded, the data should follow this structure to work with run_sclight.gat.sh:

scLightGAT_data/
├── Integrated_training/
│   └── train.h5ad
├── Independent_testing/
│   ├── GSE115978.h5ad
│   ├── GSE123139.h5ad
│   ├── GSE153935.h5ad
│   ├── GSE166555.h5ad
│   ├── Zhengsorted.h5ad
│   ├── lung_full.h5ad
│   └── sapiens_full.h5ad
└── caf.data/
    ├── caf_train.h5ad
    └── caf_test.h5ad

How to Use

1. Automated Download (Recommended)

You can use the download_hf_data.sh script provided in the scLightGAT repository to automatically fetch and place this data.

2. Manual Download

If you are manually setting up the project, download all files from this repository and place them in a directory named scLightGAT_data inside your project's data/ folder.

Project Structure Example:

scLightGAT_Project/
├── scLightGAT.main/       # Code repository
│   ├── run_sclight.gat.sh
│   └── ...
└── data/
    └── scLightGAT_data/   # This dataset (Downloaded here)
        ├── Integrated_training/
        ├── Independent_testing/
        └── caf.data/

Python Access

You can also access the files directly via huggingface_hub:

from huggingface_hub import hf_hub_download
import scanpy as sc

# Example: Load the training data
file_path = hf_hub_download(
    repo_id="Alfiechuang/scLightGAT", 
    filename="Integrated_training/train.h5ad",
    repo_type="dataset"
)
adata = sc.read_h5ad(file_path)