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A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data
Single cell RNA sequencing (scRNA-Seq) is being widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts, which need thorough cleaning. Existing denoising and imputation methods largely focus on a sin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990021/ https://www.ncbi.nlm.nih.gov/pubmed/35393428 http://dx.doi.org/10.1038/s41467-022-29576-y |
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author | Li, Hui Brouwer, Cory R. Luo, Weijun |
author_facet | Li, Hui Brouwer, Cory R. Luo, Weijun |
author_sort | Li, Hui |
collection | PubMed |
description | Single cell RNA sequencing (scRNA-Seq) is being widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts, which need thorough cleaning. Existing denoising and imputation methods largely focus on a single type of noise (i.e., dropouts) and have strong distribution assumptions which greatly limit their performance and application. Here we design and develop the AutoClass model, integrating two deep neural network components, an autoencoder, and a classifier, as to maximize both noise removal and signal retention. AutoClass is distribution agnostic as it makes no assumption on specific data distributions, hence can effectively clean a wide range of noise and artifacts. AutoClass outperforms the state-of-art methods in multiple types of scRNA-Seq data analyses, including data recovery, differential expression analysis, clustering analysis, and batch effect removal. Importantly, AutoClass is robust on key hyperparameter settings including bottleneck layer size, pre-clustering number and classifier weight. We have made AutoClass open source at: https://github.com/datapplab/AutoClass. |
format | Online Article Text |
id | pubmed-8990021 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89900212022-04-22 A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data Li, Hui Brouwer, Cory R. Luo, Weijun Nat Commun Article Single cell RNA sequencing (scRNA-Seq) is being widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts, which need thorough cleaning. Existing denoising and imputation methods largely focus on a single type of noise (i.e., dropouts) and have strong distribution assumptions which greatly limit their performance and application. Here we design and develop the AutoClass model, integrating two deep neural network components, an autoencoder, and a classifier, as to maximize both noise removal and signal retention. AutoClass is distribution agnostic as it makes no assumption on specific data distributions, hence can effectively clean a wide range of noise and artifacts. AutoClass outperforms the state-of-art methods in multiple types of scRNA-Seq data analyses, including data recovery, differential expression analysis, clustering analysis, and batch effect removal. Importantly, AutoClass is robust on key hyperparameter settings including bottleneck layer size, pre-clustering number and classifier weight. We have made AutoClass open source at: https://github.com/datapplab/AutoClass. Nature Publishing Group UK 2022-04-07 /pmc/articles/PMC8990021/ /pubmed/35393428 http://dx.doi.org/10.1038/s41467-022-29576-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Li, Hui Brouwer, Cory R. Luo, Weijun A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data |
title | A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data |
title_full | A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data |
title_fullStr | A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data |
title_full_unstemmed | A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data |
title_short | A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data |
title_sort | universal deep neural network for in-depth cleaning of single-cell rna-seq data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8990021/ https://www.ncbi.nlm.nih.gov/pubmed/35393428 http://dx.doi.org/10.1038/s41467-022-29576-y |
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