scGNN is a novel graph neural network framework for single-cell RNA-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 relationsh...
Main Authors: | Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, Hongjun Fu, Qin Ma, Dong Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2021-03-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-021-22197-x |
Similar Items
-
Author Correction: scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
by: Juexin Wang, et al.
Published: (2022-05-01) -
Use of scREAD to explore and analyze single-cell and single-nucleus RNA-seq data for Alzheimer’s disease
by: Cankun Wang, et al.
Published: (2021-06-01) -
Single-cell biological network inference using a heterogeneous graph transformer
by: Anjun Ma, et al.
Published: (2023-02-01) -
Explainable GNN-based models over knowledge graphs
by: Tena Cucala, DJ, et al.
Published: (2022) -
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
by: Shuhao Shi, et al.
Published: (2021-11-01)