scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
By Juexin Wang, A. Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, H. Fu, Qin Ma, Dong Xu. Nature Communications, 2021
446 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ab0cd1ecb81af2b474523821a0a97cb28963ce01
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Citations in the corpus
- Graph Neural Networks and Their Current Applications in Bioinformatics (2021, background): “GNNs can infer the interaction between cells (Jiahua et al., 2020; Zeng et al., 2020; Wang et al., 2021) and simulate cell differentiation (Bica et al., 2020) and disease state prediction (Ravindra et al., 2020).”
- Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine (2023, methodology): “For example, Wang et al. introduced a single-cell graph neural network (scGNN) that used GNNs to establish and aggregate cell–cell relationships which provided an effective representation of gene expression and cell–cell relationships [52].”
- Graph Representation Learning in Biomedicine and Healthcare (2021, unknown): “Cell-similarity graphs have also been created using autoencoders by first embedding gene-expression readouts and then connecting genes based on how similar their embeddings are 144 .”