Fast and precise single-cell data analysis using a hierarchical autoencoder

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, we introduce a hierarchical autoencoder that reliably extracts representative information of each cell. In an extensive analysis, we demonstrate that the approach vastly 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.

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