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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884436/ https://www.ncbi.nlm.nih.gov/pubmed/33589635 http://dx.doi.org/10.1038/s41467-021-21312-2 |
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author | Tran, Duc Nguyen, Hung Tran, Bang La Vecchia, Carlo Luu, Hung N. Nguyen, Tin |
author_facet | Tran, Duc Nguyen, Hung Tran, Bang La Vecchia, Carlo Luu, Hung N. Nguyen, Tin |
author_sort | Tran, Duc |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7884436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78844362021-02-25 Fast and precise single-cell data analysis using a hierarchical autoencoder Tran, Duc Nguyen, Hung Tran, Bang La Vecchia, Carlo Luu, Hung N. Nguyen, Tin Nat Commun Article 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. Nature Publishing Group UK 2021-02-15 /pmc/articles/PMC7884436/ /pubmed/33589635 http://dx.doi.org/10.1038/s41467-021-21312-2 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Tran, Duc Nguyen, Hung Tran, Bang La Vecchia, Carlo Luu, Hung N. Nguyen, Tin Fast and precise single-cell data analysis using a hierarchical autoencoder |
title | Fast and precise single-cell data analysis using a hierarchical autoencoder |
title_full | Fast and precise single-cell data analysis using a hierarchical autoencoder |
title_fullStr | Fast and precise single-cell data analysis using a hierarchical autoencoder |
title_full_unstemmed | Fast and precise single-cell data analysis using a hierarchical autoencoder |
title_short | Fast and precise single-cell data analysis using a hierarchical autoencoder |
title_sort | fast and precise single-cell data analysis using a hierarchical autoencoder |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7884436/ https://www.ncbi.nlm.nih.gov/pubmed/33589635 http://dx.doi.org/10.1038/s41467-021-21312-2 |
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