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...

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Autores principales:	Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, Hongjun Fu, Qin Ma, Dong Xu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/a461a5e31f27497ea7c1e605964bab0a
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spelling	oai:doaj.org-article:a461a5e31f27497ea7c1e605964bab0a2021-12-02T11:44:56ZscGNN is a novel graph neural network framework for single-cell RNA-Seq analyses10.1038/s41467-021-22197-x2041-1723https://doaj.org/article/a461a5e31f27497ea7c1e605964bab0a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22197-xhttps://doaj.org/toc/2041-1723Single-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.Juexin WangAnjun MaYuzhou ChangJianting GongYuexu JiangRen QiCankun WangHongjun FuQin MaDong XuNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Juexin Wang Anjun Ma Yuzhou Chang Jianting Gong Yuexu Jiang Ren Qi Cankun Wang Hongjun Fu Qin Ma Dong Xu scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
description	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.
format	article
author	Juexin Wang Anjun Ma Yuzhou Chang Jianting Gong Yuexu Jiang Ren Qi Cankun Wang Hongjun Fu Qin Ma Dong Xu
author_facet	Juexin Wang Anjun Ma Yuzhou Chang Jianting Gong Yuexu Jiang Ren Qi Cankun Wang Hongjun Fu Qin Ma Dong Xu
author_sort	Juexin Wang
title	scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
title_short	scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
title_full	scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
title_fullStr	scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
title_full_unstemmed	scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
title_sort	scgnn is a novel graph neural network framework for single-cell rna-seq analyses
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a461a5e31f27497ea7c1e605964bab0a
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scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses

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