AUTHOR=Zhao Jianping , Wang Na , Wang Haiyun , Zheng Chunhou , Su Yansen TITLE=SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder JOURNAL=Frontiers in Genetics VOLUME=12 YEAR=2021 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.733906 DOI=10.3389/fgene.2021.733906 ISSN=1664-8021 ABSTRACT=

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-seq data dimensionality reduction algorithm based on a hierarchical autoencoder, termed SCDRHA. The proposed SCDRHA consists of two core modules, where the first module is a deep count autoencoder (DCA) that is used to denoise data, and the second module is a graph autoencoder that projects the data into a low-dimensional space. Experimental results demonstrate that SCDRHA has better performance than existing state-of-the-art algorithms on dimension reduction and noise reduction in five real scRNA-seq datasets. Besides, SCDRHA can also dramatically improve the performance of data visualization and cell clustering.