Latent periodic process inference from single-cell RNA-seq data

Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cel...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shaoheng Liang, Fang Wang, Jincheng Han, Ken Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/82f67d25755c4db7be7422364cee5dd4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:82f67d25755c4db7be7422364cee5dd4
record_format	dspace
spelling	oai:doaj.org-article:82f67d25755c4db7be7422364cee5dd42021-12-02T15:39:08ZLatent periodic process inference from single-cell RNA-seq data10.1038/s41467-020-15295-92041-1723https://doaj.org/article/82f67d25755c4db7be7422364cee5dd42020-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-15295-9https://doaj.org/toc/2041-1723Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cell information.Shaoheng LiangFang WangJincheng HanKen ChenNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Shaoheng Liang Fang Wang Jincheng Han Ken Chen Latent periodic process inference from single-cell RNA-seq data
description	Traditional methods for determining cell type composition lack scalability, while single-cell technologies remain costly and noisy compared to bulk RNA-seq. Here, the authors present a highly efficient tool to measure cellular heterogeneity in bulk expression through robust integration of single-cell information.
format	article
author	Shaoheng Liang Fang Wang Jincheng Han Ken Chen
author_facet	Shaoheng Liang Fang Wang Jincheng Han Ken Chen
author_sort	Shaoheng Liang
title	Latent periodic process inference from single-cell RNA-seq data
title_short	Latent periodic process inference from single-cell RNA-seq data
title_full	Latent periodic process inference from single-cell RNA-seq data
title_fullStr	Latent periodic process inference from single-cell RNA-seq data
title_full_unstemmed	Latent periodic process inference from single-cell RNA-seq data
title_sort	latent periodic process inference from single-cell rna-seq data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/82f67d25755c4db7be7422364cee5dd4
work_keys_str_mv	AT shaohengliang latentperiodicprocessinferencefromsinglecellrnaseqdata AT fangwang latentperiodicprocessinferencefromsinglecellrnaseqdata AT jinchenghan latentperiodicprocessinferencefromsinglecellrnaseqdata AT kenchen latentperiodicprocessinferencefromsinglecellrnaseqdata
_version_	1718385998554988544

Latent periodic process inference from single-cell RNA-seq data

Ejemplares similares