SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder

SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder

Dimensionality reduction of high-dimensional data is crucial for single-cell RNA sequencing (scRNA-seq) visualization and clustering. One prominent challenge in scRNA-seq studies comes from the dropout events, which lead to zero-inflated data. To address this issue, in this paper, we propose a scRNA...

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Bibliographic Details
Main Authors: Jianping Zhao, Na Wang, Haiyun Wang, Chunhou Zheng, Yansen Su
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-08-01
Series:Frontiers in Genetics
Subjects:
scRNA-seq
dimensionality reduction
graph autoencoder
graph attention networks
noise reduction
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2021.733906/full

Internet

https://www.frontiersin.org/articles/10.3389/fgene.2021.733906/full

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