High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE
<|reference_start|>High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE: Spatial transcriptomics (ST) is a groundbreaking genomic technology that enables spatial localization analysis of gene expression within tissue sections. However, it is significantly limited by high costs and sparse spatial resolution. An alternative, more cost-effective strategy is to use deep learning methods to predict high-density gene expression profiles from histological images. However, existing methods struggle to capture rich image features effectively or rely on low-dimensional positional coordinates, making it difficult to accurately predict high-resolution gene expression profiles. To address these limitations, we developed HisToSGE, a method that employs a Pathology Image Large Model (PILM) to extract rich image features from histological images and utilizes a feature learning module to robustly generate high-resolution gene expression profiles. We evaluated HisToSGE on four ST datasets, comparing its performance with five state-of-the-art baseline methods. The results demonstrate that HisToSGE excels in generating high-resolution gene expression profiles and performing downstream tasks such as spatial domain identification. All code and public datasets used in this paper are available at https://github.com/wenwenmin/HisToSGE and https://zenodo.org/records/12792163.<|reference_end|>
@article{shi2024high-resolution,
    title={High-Resolution Spatial Transcriptomics from Histology Images using
  HisToSGE},
    author={Zhiceng Shi, Shuailin Xue, Fangfang Zhu and Wenwen Min},
    journal={arXiv preprint arXiv:2407.20518},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20518},
    primaryClass={eess.IV cs.AI cs.CV}
}