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.

Update

Jun 2, 2026 We corrected the radius in ST/coord/GSE144239 files (thanks to @qiqi-lu for flagging this issue.).

Feb 12, 2025 We corrected a typo in meta file (changed "Human_Brain+Kidney_10X_02212023_Visium" to "Mouse_Brain+Kidney_10X_02212023_Visium"). Please refer to meta_all_gene02122025.csv for the newest meta 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.

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

@inproceedings{NEURIPS2024_3ef2b740,
 author = {Chen, Jiawen and Zhou, Muqing and Wu, Wenrong and Zhang, Jinwei and Li, Yun and Li, Didong},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
 pages = {35796--35823},
 publisher = {Curran Associates, Inc.},
 title = {STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics},
 url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/3ef2b740cb22dcce67c20989cb3d3fce-Paper-Datasets_and_Benchmarks_Track.pdf},
 volume = {37},
 year = {2024}
}

License

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