Fast and precise single-cell data analysis using hierarchical autoencoder

Duc Tran, Hung Nguyen, Bang Tran, Carlo La Vecchia, Hung N. Luu, Tin Nguyen

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Jan 10, 2020
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Received Date: 29th December 19

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 classication, and pseudo-time inference.

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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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