Year: 2024
Type: conference-paper
Abstract: 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 which employs a Pathology Image Large Model (PILM) to extract rich image features from histological images and utilizes a feature learning block 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.
Authors: Zhiceng Shi, Shuailin Xue, Fangfang Zhu, Wenwen Min
Institutions: Yunnan University, Yunnan Open University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Award: G630115661
OpenAlex ID: W4406260087