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STimage-1K4M Dataset

Welcome to the STimage-1K4M Dataset repository. This dataset is designed to foster research in the field of spatial transcriptomics, combining high-resolution histopathology images with detailed gene expression data.

Dataset Description

STimage-1K4M consists of 1,149 spatial transcriptomics slides, totaling over 4 million spots with paired gene expression data. This dataset includes:

• Images.
• Gene expression profiles matched with high-resolution histopathology images.
• Spatial coordinates for each spot.

See example folder for an example slide from Andersson et al. (pmid: 34650042).

Getting Started

To use the STimage-1K4M dataset in your research, please access the dataset via Hugging Face. You may also fill in your email in this Google form to get a link to download the file from our FTP server.

Data structure

The data structure is organized as follows:

├── annotation              # Pathologist annotation
├── meta                    # Test files (alternatively `spec` or `tests`)
│   ├── bib.txt             # the bibtex for all studies with pmid included in the dataset
│   ├── meta_all_gene.csv   # The meta information
├── ST                      # Include all data for tech: Spatial Transcriptomics
│   ├── coord               # Include the spot coordinates & spot radius of each slide
│   ├── gene_exp            # Include the gene expression of each slide
│   └── image               # Include the image each slide
├── Visium                  # Include all data for tech: Visium, same structure as ST
├── VisiumHD                # Include all data for tech: VisiumHD, same structure as ST

Repository structure

The code for data processing and reproducing evaluation result in the paper are in Document.

Acknowledgement

The fine-tuning and evaluation codes borrows heavily from CLIP and PLIP.

Citation

@misc{chen2024stimage1k4m,
      title={STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics}, 
      author={Jiawen Chen and Muqing Zhou and Wenrong Wu and Jinwei Zhang and Yun Li and Didong Li},
      year={2024},
      eprint={2406.06393},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

All code is licensed under the MIT License - see the LICENSE.md file for details.