Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China; Department of Computer Science, Hong Kong Baptist University, Hong Kong Special Administrative Region; Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
Department of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China.
J Mol Biol. 2024 May 1;436(9):168543. doi: 10.1016/j.jmb.2024.168543. Epub 2024 Mar 18.
Cellular communication relies on the intricate interplay of signaling molecules, forming the Cell-cell Interaction network (CCI) that coordinates tissue behavior. Researchers have shown the capability of shallow neural networks in reconstructing CCI, given molecules' abundance in the Spatial Transcriptomics (ST) data. When encountering situations such as sparse connections in CCI and excessive noise, the susceptibility of shallow networks to these factors significantly impacts the accuracy of CCI reconstruction, resulting in subpar results. To reconstruct a more comprehensive and accurate CCI, we propose a novel method named Triple-Enhancement based Graph Neural Network (TENET). In TENET, three progressive enhancement mechanisms build upon each other, creating a cumulative effect. This approach can ensure the ability to capture valuable features in limited data and amplify the noise signal to facilitate the denoising effect. Additionally, the whole architecture guides the decoding reconstruction phase with integrated knowledge, which leverages the accumulated insights from each stage of enhancement to ensure a refined and comprehensive CCI reconstruction. The presented TENET has been implemented and tested on both real and synthetic ST datasets. Averagely, the CCI reconstruction using TENET achieves a 9.61% improvement in Average Precision (AP) and a 7.32% improvement in Area Under the Receiver Operating Characteristic (AUROC) compared to the existing state-of-the-art (SOTA) method. The source code and data are available at https://github.com/Yujian-Lee/TENET.
细胞通讯依赖于信号分子的复杂相互作用，形成细胞间相互作用网络（CCI），协调组织行为。研究人员已经展示了浅层神经网络在重建 CCI 方面的能力，前提是分子在空间转录组学（ST）数据中的丰度。当 CCI 中存在稀疏连接和过多噪声等情况时，浅层网络对这些因素的敏感性会显著影响 CCI 重建的准确性，导致结果不佳。为了重建更全面和准确的 CCI，我们提出了一种名为基于三重增强的图神经网络（TENET）的新方法。在 TENET 中，三个渐进式增强机制相互构建，产生累积效应。这种方法可以确保在有限的数据中捕捉有价值特征的能力，并放大噪声信号，以促进去噪效果。此外，整个架构通过集成知识引导解码重建阶段，利用每个增强阶段积累的见解来确保精细和全面的 CCI 重建。所提出的 TENET 已经在真实和合成的 ST 数据集上进行了实现和测试。平均而言，与现有的最先进（SOTA）方法相比，使用 TENET 进行 CCI 重建可将平均精度（AP）提高 9.61%，将接收者操作特征曲线下的面积（AUROC）提高 7.32%。源代码和数据可在 https://github.com/Yujian-Lee/TENET 上获得。
