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 an analysis framework, named single-cell Decomposition using Hierarchical Autoencoder (scDHA), that reliably extracts repres...

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Vydáno v:	Nature communications Ročník 12; číslo 1; s. 1029 - 10
Hlavní autoři:	Tran, Duc, Nguyen, Hung, Tran, Bang, La Vecchia, Carlo, Luu, Hung N., Nguyen, Tin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 15.02.2021 Nature Publishing Group Nature Portfolio
Témata:	631/114/1305 631/114/794 631/208/514/1949 Accuracy Animals Bayes Theorem Bayesian analysis Benchmarking Cell cycle Cell Separation - methods Cerebellum - chemistry Cerebellum - cytology Classification Clustering Data analysis Datasets Decomposition Embryo, Mammalian Gene expression Gene sequencing Genomics Humanities and Social Sciences Humans Liver - chemistry Liver - cytology Lung - chemistry Lung - cytology Methods Mice Modules Mouse Embryonic Stem Cells - chemistry Mouse Embryonic Stem Cells - cytology multidisciplinary Neural networks Neural Networks, Computer Noise Noise levels Pancreas - chemistry Pancreas - cytology Representations Retina - chemistry Retina - cytology Ribonucleic acid RNA Science Science (multidisciplinary) Segregation Sequence Analysis, RNA - statistics & numerical data Single-Cell Analysis - methods Single-Cell Analysis - statistics & numerical data Transcriptomes Unsupervised Machine Learning - statistics & numerical data Visual Cortex - chemistry Visual Cortex - cytology Visualization Zygote - chemistry Zygote - cytology
ISSN:	2041-1723, 2041-1723
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Shrnutí:	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. Accurate analysis of single-cell RNA sequencing (scRNA-seq) data is affected by issues including technical noise and high dropout rate. Here, the authors develop a hierarchical autoencoder, scDHA, which outperforms existing methods in scRNA-seq analyses such as cell segregation and classification.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-021-21312-2