Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization

TL;DR

This paper introduces a multi-view graph contrastive learning framework with HSIC-bottleneck regularization to predict spatial gene expression from histology images, effectively capturing spatial dependencies and shared features.

Contribution

It proposes a novel multi-view graph contrastive learning model with HSIC-bottleneck regularization for spatial gene expression prediction from histology images.

Findings

Improved accuracy in gene expression prediction over existing methods.

Effective modeling of spatial dependencies among tissue spots.

Enhanced shared feature extraction between histology images and gene expression data.

Abstract

The rapid development of spatial transcriptomics(ST) enables the measurement of gene expression at spatial resolution, making it possible to simultaneously profile the gene expression, spatial locations of spots, and the matched histopathological images. However, the cost for collecting ST data is much higher than acquiring histopathological images, and thus several studies attempt to predict the gene expression on ST by leveraging their corresponding histopathological images. Most of the existing image-based gene prediction models treat the prediction task on each spot of ST data independently, which ignores the spatial dependency among spots. In addition, while the histology images share phenotypic characteristics with the ST data, it is still challenge to extract such common information to help align paired image and expression representations. To address the above issues, we propose a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization(ST-GCHB) aiming at learning shared representation to help impute the gene expression of the queried imagingspots by considering their spatial dependency.