Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

Low sample numbers often limit the robustness of analyses in biomedical research. Here, the authors introduce a method to generate realistic scRNA-seq data using GANs that learn gene expression dependencies from complex samples, and show that augmenting spare cell populations improves downstream ana...

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Autores principales:	Mohamed Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, Christian F. Krebs, Stefan Bonn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/54eea9701f364c569d5dd410717e6a68
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spelling	oai:doaj.org-article:54eea9701f364c569d5dd410717e6a682021-12-02T15:39:20ZRealistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks10.1038/s41467-019-14018-z2041-1723https://doaj.org/article/54eea9701f364c569d5dd410717e6a682020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-14018-zhttps://doaj.org/toc/2041-1723Low sample numbers often limit the robustness of analyses in biomedical research. Here, the authors introduce a method to generate realistic scRNA-seq data using GANs that learn gene expression dependencies from complex samples, and show that augmenting spare cell populations improves downstream analyses.Mohamed MaroufPierre MachartVikas BansalChristoph KilianDaniel S. MagruderChristian F. KrebsStefan BonnNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Mohamed Marouf Pierre Machart Vikas Bansal Christoph Kilian Daniel S. Magruder Christian F. Krebs Stefan Bonn Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
description	Low sample numbers often limit the robustness of analyses in biomedical research. Here, the authors introduce a method to generate realistic scRNA-seq data using GANs that learn gene expression dependencies from complex samples, and show that augmenting spare cell populations improves downstream analyses.
format	article
author	Mohamed Marouf Pierre Machart Vikas Bansal Christoph Kilian Daniel S. Magruder Christian F. Krebs Stefan Bonn
author_facet	Mohamed Marouf Pierre Machart Vikas Bansal Christoph Kilian Daniel S. Magruder Christian F. Krebs Stefan Bonn
author_sort	Mohamed Marouf
title	Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
title_short	Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
title_full	Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
title_fullStr	Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
title_full_unstemmed	Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks
title_sort	realistic in silico generation and augmentation of single-cell rna-seq data using generative adversarial networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/54eea9701f364c569d5dd410717e6a68
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Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

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