PT  - JOURNAL ARTICLE
AU  - Juexin Wang
AU  - Anjun Ma
AU  - Yuzhou Chang
AU  - Jianting Gong
AU  - Yuexu Jiang
AU  - Hongjun Fu
AU  - Cankun Wang
AU  - Ren Qi
AU  - Qin Ma
AU  - Dong Xu
TI  - scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses
AID  - 10.1101/2020.08.02.233569
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.08.02.233569
4099  - http://biorxiv.org/content/early/2020/08/03/2020.08.02.233569.short
4100  - http://biorxiv.org/content/early/2020/08/03/2020.08.02.233569.full
AB  - 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 novel and powerful framework that can be applied to scRNA-Seq analyses.Competing Interest StatementThe authors have declared no competing interest.