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

Duc Tran, Hung Nguyen, Bang Tran, Carlo La Vecchia, Hung N. Luu and Tin Nguyen ()
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Duc Tran: University of Nevada Reno
Hung Nguyen: University of Nevada Reno
Bang Tran: University of Nevada Reno
Carlo La Vecchia: University of Milan
Hung N. Luu: Division of Cancer Control and Population Sciences, Hillman Cancer Center, University of Pittsburgh Medical Center
Tin Nguyen: University of Nevada Reno

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract 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 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, we repeatedly perturb the compressed space to learn a more generalized representation of the data. In an extensive analysis, we 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21312-2

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DOI: 10.1038/s41467-021-21312-2

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