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

Abstract 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 autoen...

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Autores principales: Zixiang Luo, Chenyu Xu, Zhen Zhang, Wenfei Jin
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/30dec251c0cd4590a65a1c8076f91bcf
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