通过超图神经网络从组织学图像中的基因表达预测
Gene expression prediction from histology images via hypergraph neural networks
影响因子:7.70000
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
摘要
空间转录组学揭示了复杂组织中基因的空间分布，从而为生物过程，疾病机制和药物发育提供了关键的见解。基于基于成本效益的组织学图像的基因表达的预测是一个有希望的研究领域。基因预测的现有方法显示了两个主要局限性。首先，他们忽略了细胞形态信息与基因表达之间的复杂关系。其次，这些方法并未完全利用从图像中提取的特征的不同潜在阶段。为了解决这些局限性，我们提出了一种新型的超毛神经网络模型HGGEP，以预测组织学图像的基因表达。 HGGEP包括一个梯度增强模块，以增强模型对细胞形态信息的看法。轻巧的骨干网络从图像中提取了多个潜在阶段特征，然后采用注意机制，以完善每个潜在阶段的特征的表示，并捕获其与附近功能的关系。为了探索多个潜在阶段特征之间的高阶关联，我们将它们堆叠并进食超图中以在不同尺度的特征之间建立关联。来自包括癌症和肿瘤疾病在内的疾病样本的多个数据集的实验结果表明，与现有方法相比，HGGEP模型的表现出色。
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.