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--- |
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license: mit |
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task_categories: |
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- image-feature-extraction |
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- image-segmentation |
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- image-classification |
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language: |
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- en |
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tags: |
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- biology |
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pretty_name: STimage-1K4M |
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size_categories: |
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- 100B<n<1T |
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--- |
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# STimage-1K4M Dataset |
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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. |
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## Dataset Description |
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STimage-1K4M consists of 1,149 spatial transcriptomics slides, totaling over 4 million spots with paired gene expression data. This dataset includes: |
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- Images. |
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- Gene expression profiles matched with high-resolution histopathology images. |
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- Spatial coordinates for each spot. |
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## Data structure |
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The data structure is organized as follows: |
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```bash |
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├── annotation # Pathologist annotation |
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├── meta # Test files (alternatively `spec` or `tests`) |
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│ ├── bib.txt # the bibtex for all studies with pmid included in the dataset |
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│ ├── meta_all_gene.csv # The meta information |
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├── ST # Include all data for tech: Spatial Transcriptomics |
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│ ├── coord # Include the spot coordinates & spot radius of each slide |
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│ ├── gene_exp # Include the gene expression of each slide |
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│ └── image # Include the image each slide |
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├── Visium # Include all data for tech: Visium, same structure as ST |
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├── VisiumHD # Include all data for tech: VisiumHD, same structure as ST |
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``` |
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## Repository structure |
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The code for data processing and reproducing evaluation result in the paper are in [Document](https://jiawenchenn.github.io/STimage-1K4M/docs/01-make-meta). |
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## Acknowledgement |
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The fine-tuning and evaluation codes borrows heavily from [CLIP](https://github.com/openai/CLIP/issues/83) and [PLIP](https://github.com/PathologyFoundation/plip/). |
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## Citation |
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``` |
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@misc{chen2024stimage1k4m, |
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title={STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics}, |
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author={Jiawen Chen and Muqing Zhou and Wenrong Wu and Jinwei Zhang and Yun Li and Didong Li}, |
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year={2024}, |
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eprint={2406.06393}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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## License |
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All code is licensed under the MIT License - see the LICENSE.md file for details. |
