TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics

Abstract

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.