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: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-22197-x |
| _version_ | 1818421555957334016 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T13:12:14Z |
| format | Article |
| id | doaj.art-a461a5e31f27497ea7c1e605964bab0a |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-14T13:12:14Z |
| publishDate | 2021-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-a461a5e31f27497ea7c1e605964bab0a2022-12-21T23:00:11ZengNature PortfolioNature Communications2041-17232021-03-0112111110.1038/s41467-021-22197-xscGNN is a novel graph neural network framework for single-cell RNA-Seq analysesJuexin Wang0Anjun Ma1Yuzhou Chang2Jianting Gong3Yuexu Jiang4Ren Qi5Cankun Wang6Hongjun Fu7Qin Ma8Dong Xu9Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of MissouriDepartment of Biomedical Informatics, College of Medicine, The Ohio State UniversityDepartment of Biomedical Informatics, College of Medicine, The Ohio State UniversityDepartment of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of MissouriDepartment of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of MissouriDepartment of Biomedical Informatics, College of Medicine, The Ohio State UniversityDepartment of Biomedical Informatics, College of Medicine, The Ohio State UniversityDepartment of Neuroscience, The Ohio State UniversityDepartment of Biomedical Informatics, College of Medicine, The Ohio State UniversityDepartment of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of MissouriSingle-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.https://doi.org/10.1038/s41467-021-22197-x |
| spellingShingle | 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 Nature Communications |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41467-021-22197-x |
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