Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

Computational single-cell RNA-seq analyses often face challenges in scalability, model interpretability, and confounders. Here, we show a new model to address these challenges by learning meaningful embeddings from the data that simultaneously refine gene signatures and cell functions in diverse con...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yifan Zhao, Huiyu Cai, Zuobai Zhang, Jian Tang, Yue Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7990959eb2ba4fc09e678054e84e1c54
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7990959eb2ba4fc09e678054e84e1c54
record_format	dspace
spelling	oai:doaj.org-article:7990959eb2ba4fc09e678054e84e1c542021-12-02T17:19:39ZLearning interpretable cellular and gene signature embeddings from single-cell transcriptomic data10.1038/s41467-021-25534-22041-1723https://doaj.org/article/7990959eb2ba4fc09e678054e84e1c542021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25534-2https://doaj.org/toc/2041-1723Computational single-cell RNA-seq analyses often face challenges in scalability, model interpretability, and confounders. Here, we show a new model to address these challenges by learning meaningful embeddings from the data that simultaneously refine gene signatures and cell functions in diverse conditions.Yifan ZhaoHuiyu CaiZuobai ZhangJian TangYue LiNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yifan Zhao Huiyu Cai Zuobai Zhang Jian Tang Yue Li Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
description	Computational single-cell RNA-seq analyses often face challenges in scalability, model interpretability, and confounders. Here, we show a new model to address these challenges by learning meaningful embeddings from the data that simultaneously refine gene signatures and cell functions in diverse conditions.
format	article
author	Yifan Zhao Huiyu Cai Zuobai Zhang Jian Tang Yue Li
author_facet	Yifan Zhao Huiyu Cai Zuobai Zhang Jian Tang Yue Li
author_sort	Yifan Zhao
title	Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
title_short	Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
title_full	Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
title_fullStr	Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
title_full_unstemmed	Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
title_sort	learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7990959eb2ba4fc09e678054e84e1c54
work_keys_str_mv	AT yifanzhao learninginterpretablecellularandgenesignatureembeddingsfromsinglecelltranscriptomicdata AT huiyucai learninginterpretablecellularandgenesignatureembeddingsfromsinglecelltranscriptomicdata AT zuobaizhang learninginterpretablecellularandgenesignatureembeddingsfromsinglecelltranscriptomicdata AT jiantang learninginterpretablecellularandgenesignatureembeddingsfromsinglecelltranscriptomicdata AT yueli learninginterpretablecellularandgenesignatureembeddingsfromsinglecelltranscriptomicdata
_version_	1718381034411655168

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data

Ejemplares similares