A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data

How cell clusters are defined in single-cell sequencing data has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. Here, the authors present a new approach that enables the prediction of differentially expressed genes without relying...

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Autores principales:	Alexis Vandenbon, Diego Diez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1be0aefe11034fad8a85057c8b048b54
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spelling	oai:doaj.org-article:1be0aefe11034fad8a85057c8b048b542021-12-02T19:02:36ZA clustering-independent method for finding differentially expressed genes in single-cell transcriptome data10.1038/s41467-020-17900-32041-1723https://doaj.org/article/1be0aefe11034fad8a85057c8b048b542020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17900-3https://doaj.org/toc/2041-1723How cell clusters are defined in single-cell sequencing data has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. Here, the authors present a new approach that enables the prediction of differentially expressed genes without relying on explicit clustering of cells.Alexis VandenbonDiego DiezNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Alexis Vandenbon Diego Diez A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
description	How cell clusters are defined in single-cell sequencing data has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. Here, the authors present a new approach that enables the prediction of differentially expressed genes without relying on explicit clustering of cells.
format	article
author	Alexis Vandenbon Diego Diez
author_facet	Alexis Vandenbon Diego Diez
author_sort	Alexis Vandenbon
title	A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
title_short	A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
title_full	A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
title_fullStr	A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
title_full_unstemmed	A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
title_sort	clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1be0aefe11034fad8a85057c8b048b54
work_keys_str_mv	AT alexisvandenbon aclusteringindependentmethodforfindingdifferentiallyexpressedgenesinsinglecelltranscriptomedata AT diegodiez aclusteringindependentmethodforfindingdifferentiallyexpressedgenesinsinglecelltranscriptomedata AT alexisvandenbon clusteringindependentmethodforfindingdifferentiallyexpressedgenesinsinglecelltranscriptomedata AT diegodiez clusteringindependentmethodforfindingdifferentiallyexpressedgenesinsinglecelltranscriptomedata
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A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data

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