A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, researchers from the University of Nevada Reno introduce an analysis framework, named single-cell Decomposition using Hierarchical Autoencoder (scDHA), that reliably extracts representative information of each cell. The scDHA pipeline consists of two core modules. The first module is a non-negative kernel autoencoder able to remove genes or components that have insignificant contributions to the part-based representation of the data. The second module is a stacked Bayesian autoencoder that projects the data onto a low-dimensional space (compressed). To diminish the tendency to overfit of neural networks, the researchers repeatedly perturb the compressed space to learn a more generalized representation of the data. In an extensive analysis, they demonstrate that scDHA outperforms state-of-the-art techniques in many research sub-fields of scRNA-seq analysis, including cell segregation through unsupervised learning, visualization of transcriptome landscape, cell classification, and pseudo-time inference.
Schematic overview of scDHA and applications
Cell segregation through unsupervised learning, visualization, pseudo-temporal ordering, and cell classification
Availability – The scDHA package is available as an independent software at https://github.com/duct317/scDHA.