A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder

Dimensionality reduction is crucial for the visualization and interpretation of the high-dimensional single-cell RNA sequencing (scRNA-seq) data. However, preserving topological structure among cells to low dimensional space remains a challenge. Here, we present the single-cell graph autoencoder (sc...

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Detalles Bibliográficos
Autores principales: Luo, Zixiang, Xu, Chenyu, Zhang, Zhen, Jin, Wenfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8501122/
https://www.ncbi.nlm.nih.gov/pubmed/34625592
http://dx.doi.org/10.1038/s41598-021-99003-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8501122/
https://www.ncbi.nlm.nih.gov/pubmed/34625592
http://dx.doi.org/10.1038/s41598-021-99003-7

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