Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks

Single-cell RNA sequencing technology promotes the profiling of single-cell transcriptomes at an unprecedented throughput and resolution. However, in scRNA-seq studies, only a low amount of sequenced mRNA in each cell leads to missing detection for a portion of mRNA molecules, i.e. the dropout probl...

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Detalles Bibliográficos
Autores principales: Rao, Jiahua, Zhou, Xiang, Lu, Yutong, Zhao, Huiying, Yang, Yuedong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8091052/
https://www.ncbi.nlm.nih.gov/pubmed/33997678
http://dx.doi.org/10.1016/j.isci.2021.102393

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8091052/
https://www.ncbi.nlm.nih.gov/pubmed/33997678
http://dx.doi.org/10.1016/j.isci.2021.102393

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