Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq data, such as cellular heterogeneity and high sparsity caused by dropout events, traditional network inference methods may not be suitable for scRNA-seq data. In this study, we introduce a novel joint Gaussian copula graphical model (JGCGM) to jointly estimate multiple gene networks for multiple cell subgroups from scRNA-seq data. Our model can deal with non-Gaussian data with missing values, and identify the common and unique network structures of multiple cell subgroups, which is suitable for scRNA-seq data. Extensive experiments on synthetic data demonstrate that our proposed model outperforms other compared state-of-the-art network inference models. We apply our model to real scRNA-seq data sets to infer gene networks of different cell subgroups. Hub genes in the estimated gene networks are found to be biological significance.

从基因表达数据推断基因网络对于理解细胞内的功能组织非常重要。随着单细胞RNA测序（scRNA-seq）数据的积累，推断单细胞水平的基因网络成为可能。然而，由于scRNA-seq数据的特点，例如细胞异质性和dropout事件导致的高稀疏性，传统的网络推理方法可能不适合scRNA-seq数据。在本研究中，我们引入了一种新颖的联合高斯关联图形模型（JGCGM），用于根据 scRNA-seq 数据联合估计多个细胞亚组的多个基因网络。我们的模型可以处理缺失值的非高斯数据，并识别多个细胞亚群的共同和独特的网络结构，适用于 scRNA-seq 数据。对合成数据的大量实验表明，我们提出的模型优于其他最先进的网络推理模型。我们将我们的模型应用于真实的 scRNA-seq 数据集，以推断不同细胞亚群的基因网络。发现估计的基因网络中的中心基因具有生物学意义。