scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.
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DOI：
10.1038/s41467-021-22197-x
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发表时间：
2021-03-25
影响因子：
16.6
通讯作者：
Xu D
中科院分区：
文献类型：
--
作者：
Wang J;Ma A;Chang Y;Gong J;Jiang Y;Qi R;Wang C;Fu H;Ma Q;Xu D
单细胞RNA测序（scRNA-Seq）广泛用于揭示组织、生物体和复杂疾病的异质性和动态，但其分析仍然面临多重巨大挑战，包括测序稀疏性和基因表达中复杂的差异模式。我们引入 scGNN（单细胞图神经网络）来为 scRNA-Seq 分析提供无假设深度学习框架。该框架通过图神经网络制定和聚合细胞间关系，并使用左截断混合高斯模型对异质基因表达模式进行建模。 scGNN 集成了三个迭代多模态自动编码器，在四个基准 scRNA-Seq 数据集上优于现有的基因插补和细胞聚类工具。在一项使用来自死后脑组织的 13,214 个单核进行的阿尔茨海默病研究中，scGNN 成功阐明了与疾病相关的神经发育和差异机制。 scGNN 提供了基因表达和细胞间关系的有效表示。它也是一个强大的框架，可应用于一般的 scRNA-Seq 分析。单细胞 RNA-Seq 面临测序稀疏性的异质性和基因表达复杂差异模式的问题。在这里，作者介绍了一种基于无假设深度学习框架的图神经网络，作为基因表达和细胞间关系的有效表示。
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. Single-cell RNA-Seq suffers from heterogeneity in sequencing sparsity and complex differential patterns in gene expression. Here, the authors introduce a graph neural network based on a hypothesis-free deep learning framework as an effective representation of gene expression and cell–cell relationships.
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影响因子：
64.5
作者：
Levine JH;Simonds EF;Bendall SC;Davis KL;Amir el-AD;Tadmor MD;Litvin O;Fienberg HG;Jager A;Zunder ER;Finck R;Gedman AL;Radtke I;Downing JR;Pe'er D;Nolan GP
通讯作者：
Nolan GP
影响因子：
46.9
作者：
Han, Arnold;Glanville, Jacob;Hansmann, Leo;Davis, Mark M.
通讯作者：
Davis, Mark M.
影响因子：
48
作者：
Amodio, Matthew;van Dijk, David;Srinivasan, Krishnan;Chen, William S.;Mohsen, Hussein;Moon, Kevin R.;Campbell, Allison;Zhao, Yujiao;Wang, Xiaomei;Venkataswamy, Manjunatha;Desai, Anita;Ravi, V.;Kumar, Priti;Montgomery, Ruth;Wolf, Guy;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
影响因子：
7
作者：
Elyanow, Rebecca;Dumitrascu, Bianca;Raphael, Benjamin J.
通讯作者：
Raphael, Benjamin J.
影响因子：
15.2
作者：
Fang C;Xu D;Su J;Dry JR;Linghu B
通讯作者：
Linghu B