MagNet: Multi-Level Attention Graph Network for Predicting High-Resolution Spatial Transcriptomics

TL;DR

MagNet is a novel multi-level attention graph network that accurately predicts high-resolution spatial transcriptomics data by integrating multi-resolution image features and neighborhood information, surpassing existing methods.

Contribution

Introduces MagNet, a multi-level attention graph network that effectively predicts high-resolution spatial transcriptomics, addressing the limitations of low-resolution focused models.

Findings

Achieves state-of-the-art performance at multiple resolution levels.

Effectively integrates multi-resolution image features.

Provides a new benchmark for high-resolution spatial transcriptomics.

Abstract

The rapid development of spatial transcriptomics (ST) offers new opportunities to explore the gene expression patterns within the spatial microenvironment. Current research integrates pathological images to infer gene expression, addressing the high costs and time-consuming processes to generate spatial transcriptomics data. However, as spatial transcriptomics resolution continues to improve, existing methods remain primarily focused on gene expression prediction at low-resolution spot levels. These methods face significant challenges, especially the information bottleneck, when they are applied to high-resolution HD data. To bridge this gap, this paper introduces MagNet, a multi-level attention graph network designed for accurate prediction of high-resolution HD data. MagNet employs cross-attention layers to integrate features from multi-resolution image patches hierarchically and utilizes a GAT-Transformer module to aggregate neighborhood information. By integrating multilevel features, MagNet overcomes the limitations posed by low-resolution inputs in predicting high-resolution gene expression. We systematically evaluated MagNet and existing ST prediction models on both a private spatial transcriptomics dataset and a public dataset at three different resolution levels. The results demonstrate that MagNet achieves state-of-the-art performance at both spot level and high-resolution bin levels, providing a novel methodology and benchmark for future research and applications in high-resolution HD-level spatial transcriptomics. Code is available at https://github.com/Junchao-Zhu/MagNet.