Duan Ziheng, Xu Siwei, Lee Cheyu, Riffle Dylan, Zhang Jing
University of California, Irvine, Irvine, CA, USA.
Proc ACM Int Conf Inf Knowl Manag. 2024 Oct;2024:538-548. doi: 10.1145/3627673.3679574. Epub 2024 Oct 21.
Spatial transcriptomics has transformed genomic research by measuring spatially resolved gene expressions, allowing us to investigate how cells adapt to their microenvironment via modulating their expressed genes. This essential process usually starts from cell-cell communication (CCC) via ligand-receptor (LR) interaction, leading to regulatory changes within the receiver cell. However, few methods were developed to connect them to provide biological insights into intercellular regulation. To fill this gap, we propose iMiracle, an iterative multi-view graph neural network that models each cell's intercellular regulation with three key features. Firstly, iMiracle integrates inter- and intra-cellular networks to jointly estimate - and -driven gene expressions. Optionally, it allows prior knowledge of intra-cellular networks as pre-structured masks to maintain biological relevance. Secondly, iMiracle employs iterative learning to overcome the sparsity of spatial transcriptomic data and gradually fill in the missing edges in the CCC network. Thirdly, iMiracle infers a cell-specific ligand-gene regulatory score based on the contributions of different LR pairs to interpret inter-cellular regulation. We applied iMiracle to nine simulated and eight real datasets from three sequencing platforms and demonstrated that iMiracle consistently outperformed ten methods in gene expression imputation and four methods in regulatory score inference. Lastly, we developed iMiracle as an open-source software and anticipate that it can be a powerful tool in decoding the complexities of inter-cellular transcriptional regulation.
空间转录组学通过测量空间分辨的基因表达改变了基因组研究，使我们能够研究细胞如何通过调节其表达的基因来适应微环境。这一基本过程通常始于通过配体-受体（LR）相互作用的细胞间通讯（CCC），从而导致受体细胞内的调控变化。然而，很少有方法能将它们联系起来，以提供对细胞间调控的生物学见解。为了填补这一空白，我们提出了iMiracle，这是一种迭代多视图图神经网络，它通过三个关键特征对每个细胞的细胞间调控进行建模。首先，iMiracle整合细胞间和细胞内网络，以联合估计基因表达并由其驱动。可选地，它允许将细胞内网络的先验知识作为预结构化掩码，以保持生物学相关性。其次，iMiracle采用迭代学习来克服空间转录组数据的稀疏性，并逐步填补CCC网络中缺失的边。第三，iMiracle根据不同LR对的贡献推断细胞特异性配体-基因调控分数，以解释细胞间调控。我们将iMiracle应用于来自三个测序平台的九个模拟数据集和八个真实数据集，并证明iMiracle在基因表达插补方面始终优于十种方法，在调控分数推断方面优于四种方法。最后，我们将iMiracle开发为开源软件，并预计它将成为解码细胞间转录调控复杂性的有力工具。
