scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses

Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, Hongjun Fu, Qin Ma () and Dong Xu ()
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Juexin Wang: University of Missouri
Anjun Ma: The Ohio State University
Yuzhou Chang: The Ohio State University
Jianting Gong: University of Missouri
Yuexu Jiang: University of Missouri
Ren Qi: The Ohio State University
Cankun Wang: The Ohio State University
Hongjun Fu: The Ohio State University
Qin Ma: The Ohio State University
Dong Xu: University of Missouri

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-22197-x

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