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.