Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning
Journal:
arXiv
Published Date:
Jun 10, 2025
Abstract
Spatial transcriptomics is a technology that captures gene expression levels
at different spatial locations, widely used in tumor microenvironment analysis
and molecular profiling of histopathology, providing valuable insights into
resolving gene expression and clinical diagnosis of cancer. Due to the high
cost of data acquisition, large-scale spatial transcriptomics data remain
challenging to obtain. In this study, we develop a contrastive learning-based
deep learning method to predict spatially resolved gene expression from
whole-slide images. Evaluation across six different disease datasets
demonstrates that, compared to existing studies, our method improves Pearson
Correlation Coefficient (PCC) in the prediction of highly expressed genes,
highly variable genes, and marker genes by 6.27%, 6.11%, and 11.26%
respectively. Further analysis indicates that our method preserves gene-gene
correlations and applies to datasets with limited samples. Additionally, our
method exhibits potential in cancer tissue localization based on biomarker
expression.
