This software package implements iStar (Inferring Supper-resolution Tissue ARchitecture), which enhances the spatial resolution of spatial transcriptomic data from a spot-level to a near-single-cell level. The iStar method is presented in the following paper:

Daiwei Zhang, Amelia Schroeder, Hanying Yan, Haochen Yang, Jian Hu, Michelle Y. Y. Lee, Kyung S. Cho, Katalin Susztak, George X. Xu, Michael D. Feldman, Edward B. Lee, Emma E. Furth, Linghua Wang, Mingyao Li. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nature Biotechnology (2024). https://doi.org/10.1038/s41587-023-02019-9

To run the demo,

# Use Python 3.9 or above
pip install -r requirements.txt
./run_demo.sh

Using GPUs is highly recommended.

• he-raw.jpg: Raw histology image
• cnts.tsv: Gene count matrix.
  • Row 1: Gene names.
  • Row 2 and after: Each row is a spot.
  • Column 1: Spot ID.
  • Column 2 and after: Each column is a gene.
• locs-raw.tsv: Spot locations
  • Row 1: Header
  • Row 2 and after: Each row is a spot. Must match rows in cnts.tsv
  • Column 1: Spot ID
  • Column 2: x-coordinate (horizontal axis). Must be in the same space as axis-1 (column) of the array indices of pixels in he-raw.jpg.
  • Column 2: y-coordinate (vertical axis). Must be in the same space as axis-0 (row) of the array indices of pixels in he-raw.jpg.
• pixel-size-raw.txt: Side length (in micrometers) of pixels in he-raw.jpg. This value is usually between 0.1 and 1.0.
  • For Visium data, this value can be approximated by 8000 / 2000 * tissue_hires_scalef, where tissue_hires_scalef is stored in scalefactors_json.json.
• radius-raw.txt: Number of pixels per spot radius in he-raw.jpg.
  • For Visium data, this value can be computed by spot_diameter_fullres * 0.5, where spot_diameter_fullres is stored in scalefactors_json.json, and should be close to 55 * 0.5 / pixel_size_raw.

The software package is licensed under GPL-3.0. For commercial use, please contact Daiwei (David) Zhang and Mingyao Li.

The codes for iStar are written by Daiwei (David) Zhang and under active development. Please open an issue on GitHub if you have any questions about the software package.

The codebase for the hierarchical vision transformer is built upon Vision Transformer (as implemented by Hugging Face), DINO, and HIPT. We thank the authors for releasing the codes and the model weights.

If you find this work useful, please consider citing

@article{zhang2024inferring,
  title = {Inferring Super-Resolution Tissue Architecture by Integrating Spatial Transcriptomics with Histology},
  author = {Zhang, Daiwei and Schroeder, Amelia and Yan, Hanying and Yang, Haochen and Hu, Jian and Lee, Michelle Y. Y. and Cho, Kyung S. and Susztak, Katalin and Xu, George X. and Feldman, Michael D. and Lee, Edward B. and Furth, Emma E. and Wang, Linghua and Li, Mingyao},
  year = {2024},
  month = jan,
  journal = {Nature Biotechnology},
  pages = {1--6},
  doi = {10.1038/s41587-023-02019-9},
}

as well as Vision Transformer, DINO, and HIPT.