CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks

MOTIVATION: Single-cell RNA sequencing allows high-resolution views of individual cells for libraries of up to millions of samples, thus motivating the use of deep learning for analysis. In this study, we introduce the use of graph neural networks for the unsupervised exploration of scRNA-seq data b...

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Detalles Bibliográficos
Autores principales: Buterez, David, Bica, Ioana, Tariq, Ifrah, Andrés-Terré, Helena, Liò, Pietro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825872/
https://www.ncbi.nlm.nih.gov/pubmed/34864884
http://dx.doi.org/10.1093/bioinformatics/btab804

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8825872/
https://www.ncbi.nlm.nih.gov/pubmed/34864884
http://dx.doi.org/10.1093/bioinformatics/btab804

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