利用超图神经网络从组织学图像预测基因表达

Gene expression prediction from histology images via hypergraph neural networks

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BRIEFINGS IN BIOINFORMATICS

影响因子:7.7

分区:生物学2区 / 数学与计算生物学1区生化研究方法2区

发表日期:2024 Sep 23

作者: Bo Li, Yong Zhang, Qing Wang, Chengyang Zhang, Mengran Li, Guangyu Wang, Qianqian Song

DOI: 10.1093/bib/bbae500

摘要

空间转录组学揭示了复杂组织中基因的空间分布，为理解生物过程、疾病机制和药物开发提供了关键洞察。基于成本效益高的组织学图像预测基因表达是一个充满潜力但具有挑战性的研究领域。现有的基因预测方法存在两个主要限制：一是忽略了细胞形态信息与基因表达之间的复杂关系；二是未能充分利用从图像中提取的不同潜在特征阶段。为解决这些问题，我们提出了一种新颖的超图神经网络模型HGGEP，用于从组织学图像预测基因表达。HGGEP包括一个梯度增强模块，用于提升模型对细胞形态信息的感知能力；一个轻量级骨干网络用以提取多重潜在特征，随后通过注意机制优化每个潜在阶段的特征表示，并捕获它们与邻近特征的关系。为了探索多重潜在特征之间的高阶关联，我们将这些特征堆叠后输入超图，以建立不同尺度特征间的关联。多项来自癌症及肿瘤样本的多数据集实验结果显示，我们的HGGEP模型优于现有方法。

Abstract

Spatial transcriptomics reveals the spatial distribution of genes in complex tissues, providing crucial insights into biological processes, disease mechanisms, and drug development. The prediction of gene expression based on cost-effective histology images is a promising yet challenging field of research. Existing methods for gene prediction from histology images exhibit two major limitations. First, they ignore the intricate relationship between cell morphological information and gene expression. Second, these methods do not fully utilize the different latent stages of features extracted from the images. To address these limitations, we propose a novel hypergraph neural network model, HGGEP, to predict gene expressions from histology images. HGGEP includes a gradient enhancement module to enhance the model's perception of cell morphological information. A lightweight backbone network extracts multiple latent stage features from the image, followed by attention mechanisms to refine the representation of features at each latent stage and capture their relations with nearby features. To explore higher-order associations among multiple latent stage features, we stack them and feed into the hypergraph to establish associations among features at different scales. Experimental results on multiple datasets from disease samples including cancers and tumor disease, demonstrate the superior performance of our HGGEP model than existing methods.