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Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders

Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene...

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Detalles Bibliográficos
Autores principales: Wang, Yuge, Zhao, Hongyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007392/
https://www.ncbi.nlm.nih.gov/pubmed/35363784
http://dx.doi.org/10.1371/journal.pcbi.1010025
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author Wang, Yuge
Zhao, Hongyu
author_facet Wang, Yuge
Zhao, Hongyu
author_sort Wang, Yuge
collection PubMed
description Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset.
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spelling pubmed-90073922022-04-14 Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders Wang, Yuge Zhao, Hongyu PLoS Comput Biol Research Article Advances in single-cell RNA sequencing (scRNA-seq) have led to successes in discovering novel cell types and understanding cellular heterogeneity among complex cell populations through cluster analysis. However, cluster analysis is not able to reveal continuous spectrum of states and underlying gene expression programs (GEPs) shared across cell types. We introduce scAAnet, an autoencoder for single-cell non-linear archetypal analysis, to identify GEPs and infer the relative activity of each GEP across cells. We use a count distribution-based loss term to account for the sparsity and overdispersion of the raw count data and add an archetypal constraint to the loss function of scAAnet. We first show that scAAnet outperforms existing methods for archetypal analysis across different metrics through simulations. We then demonstrate the ability of scAAnet to extract biologically meaningful GEPs using publicly available scRNA-seq datasets including a pancreatic islet dataset, a lung idiopathic pulmonary fibrosis dataset and a prefrontal cortex dataset. Public Library of Science 2022-04-01 /pmc/articles/PMC9007392/ /pubmed/35363784 http://dx.doi.org/10.1371/journal.pcbi.1010025 Text en © 2022 Wang, Zhao https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Yuge
Zhao, Hongyu
Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
title Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
title_full Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
title_fullStr Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
title_full_unstemmed Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
title_short Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders
title_sort non-linear archetypal analysis of single-cell rna-seq data by deep autoencoders
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007392/
https://www.ncbi.nlm.nih.gov/pubmed/35363784
http://dx.doi.org/10.1371/journal.pcbi.1010025
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