Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
Nat Commun. 2021 Mar 25;12(1):1882. doi: 10.1038/s41467-021-22197-x.
Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer's disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a powerful framework that can be applied to general scRNA-Seq analyses.
单细胞 RNA 测序（scRNA-Seq）被广泛用于揭示组织、生物和复杂疾病的异质性和动态，但它的分析仍然面临着多个重大挑战，包括测序稀疏和基因表达的复杂差异模式。我们引入了 scGNN（单细胞图神经网络），为 scRNA-Seq 分析提供了一个无假设的深度学习框架。该框架使用图神经网络来构建和聚合细胞间关系，并使用左截断混合高斯模型来建模异质基因表达模式。scGNN 集成了三个迭代的多模态自编码器，在四个基准 scRNA-Seq 数据集上的基因推断和细胞聚类方面优于现有工具。在一项涉及 13214 个来自死后脑组织的单核细胞的阿尔茨海默病研究中，scGNN 成功地说明了与疾病相关的神经发育和差异机制。scGNN 提供了一种有效的基因表达和细胞间关系表示方法。它也是一个强大的框架，可以应用于一般的 scRNA-Seq 分析。
